Data Scientist jobs in Israel

Description Minute is a global technology and content company built for the future of sports consumption. Minute’s proprietary technology platform enables the creation, distribution, and monetization of digital content experiences, powering Minute’s portfolio of trusted content brands, including Sports Illustrated, The Players' Tribune, and FanSided, as well as hundreds of sports leagues, teams, broadcasters, third-party publishers, and advertisers. The technology platform includes (i) STN Video, an online video platform (OVP) that provides access to a robust sports highlights rights portfolio. Minute is building the future of how the world connects with sport, powered by innovation, shared globally, and trusted by millions of fans and the partners we serve. For more information, visit www.minutegroup.com. The Role: We’re looking for an experienced, rigorous Data Scientist to join our monetization data team in Tel Aviv. You’ll lead dynamic price-floor optimization strategies for programmatic display advertising across our global inventory, build models and experiments that determine which on-page experiences we show each user, and develop strategies to maximize user lifetime value while strengthening engagement and retention. You’ll also shape and manage the roadmap for our internal experimentation platform, enabling rigorous testing across the business. This is publisher-side data science, focused on our own audiences and those of our partners. The bar that matters most here is measurement, proving credibly that a change actually moved revenue before it ships. Responsibilities: • Design and build models for supply-side monetization optimization, user lifetime-value, retention, and ARPU predictions, while measuring revenue impact. • Analyze large-scale event data and maintain metric foundations. • Partner with cross-functional teams to deploy models into production. • Communicate findings and business impact to stakeholders. • Provide technical leadership and mentorship. • Monitor industry trends in programmatic advertising and ad-tech. Requirements • 4+ years in data science / ML applied to a data-heavy, revenue-facing domain - ad-tech, programmatic monetization, marketplaces, pricing, or a comparable real-money optimization problem. • Strong causal-inference and experimentation skills. This is the core of the role. • Fluent SQL on large event datasets and hands-on Python for modeling and analysis. • Solid grounding in the relevant ML families - regression, tree-based models, online optimization / multi-armed & contextual bandits, and classification/segmentation. • In-depth understanding of programmatic advertising from the supply side - RTB, auction mechanics, header bidding, price floors, and how publisher yield is actually made. • Ability to design experiments that measure model effectiveness in production, and to own the definitions (metrics, identity, attribution) they rest on. • Experience integrating models into production systems with engineering, in a fast, iterative environment. • Proven ability to architect and scale models processing high-volume, real-time event streams. • Excellent communication - able to turn a complex monetization result into a clear recommendation a non-technical stakeholder can decide on. • Fluent English, written and spoken. Advantageous: • Hands-on experience with publisher-side monetization/yield optimization, header bidding, or price-floor / seller-side auction optimization specifically. • Familiarity with the Prebid stack (client & server). • Subscription/membership/LTV modeling - retention, churn, and balancing recurring revenue against ad revenue. • Working knowledge of GA4 / behavioral analytics and first-party identity resolution. • Prior experience mentoring or leading a small data-science team. Benefits: • Career development opportunities & different skill-based workshops are offered each quarter. • Gradual return from parental leave. • Fun company events and celebrations. • Community involvement opportunities, both in-person and remote. We're not sure you're 100% qualified, but are you up for the challenge? We want you to apply! Minute is committed to creating a diverse and inclusive work environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Check the URL and email address of any correspondence with Minute. If it is not from @minutegroup.com, treat it as fraudulent. To learn how to protect yourself from recruitment fraud, click here.

DT (NASDAQ: APPS) is unifying the full mobile experience. Our growth platform powers the mobile ecosystem by bringing together advertisers, app developers, and device partners, to create a unique and premium value exchange, that results in a personalized and seamless mobile device experience. At DT, we move fast, think big, and build collaboratively. We believe great ideas can come from anywhere, and we trust our teams to take ownership, challenge convention, and make an impact from day one. Here, you’ll have the opportunity to grow your career while contributing to products and experiences used by millions around the world. With us, you will enjoy competitive compensation, equity awards, and a comprehensive benefits package designed to invest in your total well-being, all within a hybrid and flexible work environment. The Mission: Engineering the Brain of DT Exchange At Digital Turbine, "Real-Time" is our reality - handling billions of events with millisecond-level constraints. Our DS team doesn't just build models; we build the algorithmic heartbeat of a massive global marketplace. We are currently in a peak phase of infrastructure evolution, and we need a Senior partner to help lead the charge. The Role: A True Problem Solver We’re looking for a Senior DS who thrives on End-to-End ownership. You aren't just given a dataset; you identify the business opportunity, architect the mathematical solution, and write the high-quality, production-ready code that brings it to life. This is the ultimate "DS Candy Store" for those who love research but live for the adrenaline of seeing their code drive real revenue. Who You Are: • Have AdTech experience and understand the thrill of low-latency environments and high-stakes auction dynamics • The Scientist-Engineer Hybrid: You have a 5+ year track record of delivering DS projects to production. An MSc/PhD in a quantitative field is a must • Tech Mastery: Expert-level Python (production-grade) and the ability to build for scale (Spark, ONNX - great advantage) • The "Nose for Value": You are business-oriented and independent • You don't just solve equations; you solve business problems and know how to measure your success • Mentor & Partner: You lead by example, elevating the team’s technical bar through code reviews and collaborative design What You’ll Do: • End-to-End Ownership: Own the entire lifecycle: from defining the business problem and KPI to deploying and monitoring the solution in production • High-Scale Architecture: Design and implement ML models & handling high-cardinality data at massive scale • Engineering Excellence: Write clean, modular, and scalable code. We believe great Data Science is built on a foundation of great Software Engineering • Strategic Leadership: Mentor the team through complex technical hurdles and contribute directly to our algorithmic roadmap Why Digital Turbine? Because here, your impact is immediate and measurable. You’ll have the autonomy of a startup with the data-rich environment of a global giant. If you’re ready to move from the whiteboard to the "vein" of the product, let’s talk! DT (NASDAQ: APPS) is a mobile growth platform that powers superior experiences and outcomes for advertisers, app developers, carriers, OEMs, and publishers worldwide. Our technology connects brands and apps with consumers across devices, apps, and mobile environments — helping partners drive awareness, acquisition, engagement, and monetization through intelligent, integrated experiences. Headquartered in Austin, Texas, DT operates globally across North America, EMEA, APAC, and LATAM. DT is an equal opportunity employer committed to fostering an inclusive and diverse workplace. We welcome people of different backgrounds, experiences, abilities, and perspectives, and believe teams with different points of view build better products, ideas, and experiences.

Description Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors. We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis. You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models. We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis. Key Responsibilities • Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks. • Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis. • Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns. • Develop ML models and predictive algorithms to automate red-teaming. • Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers. • Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility. • Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry). Requirements Must-Have • M.Sc. in CS/EE/Math/Statistics or a related field; Ph.D. is an advantage. • 5+ years of hands-on data science or ML experience, programming in Python or R, and SQL. • 3+ years of experience with Scikit-Learn and PyTorch. • Hands-on experience in GenAI red teaming, adversarial testing, or LLM security. • Hands-on experience building and deploying LLM-powered or ML systems in production. • Strong understanding of LLM design patterns, including prompt engineering, tool calling, structured outputs, and RAG. • Strong grasp of clustering, embeddings/NLP, semantic similarity, and anomaly/novelty detection. • Experience building classification, ranking, or prioritization systems. • Solid background in statistics, experimental design, and model evaluation. • Experience with Cradle, Apache Hadoop, and Spark, and with scaling ETL and data pipelines. Nice-to-Have • Experience building LLM agents or multi-agent workflows with frameworks such as LangGraph, LangChain, or OpenAI Agents SDK. • Familiarity with evaluation frameworks, MLOps pipelines, and AI observability tooling. • Data visualization with Tableau, Power BI, or matplotlib. • Experience with AWS, including Lambda, S3, and EC2. • Experience running inference on GenAI models via Hugging Face, Bedrock, and Azure. • Background in Trust & Safety, security, or regulated environments. Alice is the leading provider of security and safety solutions for online experiences, safeguarding more than 3 billion users, top foundation models, and the world's largest enterprises every day. Through Real-Time Guardrails and continuous Red Teaming, backed by deep threat intelligence and coverage of 117+ languages, Alice helps AI, security, and policy teams build GenAI products that stay safe at global scale. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description We are hiring a Principal Data Scientist for the Data Science group, reporting directly to the VP Engineering. This is a senior individual-contributor leadership role: you own the technical direction and architecture of what the group ships - real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath - and you lead the hardest projects end to end, hands on in the code. What you'll own A large Python, Rust, and PySpark monorepo, dozens of production services and shared libraries, across three connected domains: Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through our in-house ActiveServe framework over NVIDIA Triton and a Rust detection service, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. GenAI safety and red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives, and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok). Data platform and MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do • Own the GenAI safety roadmap - decide what we build with LLMs and what we build around them: guardrails and LLM-as-judge adjudication, prompt-injection and jailbreak detection, policy authoring, and the automated red-teaming that measures attack-success rate against customers' applications. Choose which model families and providers we use, fine-tune, or retire, and what each has to prove before it reaches customer traffic. • Own how we evaluate GenAI systems - benchmark and dataset design over real production traffic, LLM-jury labeling protocols, multilingual, multi-turn, and multimodal coverage, calibration and threshold selection, and the error analysis that turns a metric into a fix. Run the evaluations that separate a real shift in what these models can do from a launch post, and keep quality honest in production as traffic, prompts, and attacks drift. • Lead the hardest GenAI work end to end, and grow the craft - take the ambiguous, high-stakes projects from framing through prototype, offline evaluation, shadow traffic, and rollout; mentor data scientists and ML engineers through design, experiment, and model review; and translate model behavior into what Product and customers care about: accuracy, latency, cost, and enterprise readiness. Requirements What we're looking for • ML technical leadership - a proven tech lead, senior data scientist, or ML engineer who has taken multi-person, multi-quarter ML initiatives from framing to production without formal authority, with strong Python and the written communication to make the case in a design or an evaluation. • Deep understanding of LLM technology - how these models are trained, adapted, and served, what prompting, fine-tuning, RAG, and agentic patterns each buy you, where they fail, and how to measure quality, latency, and cost. • AI-augmented engineering - deep, daily fluency with an AI coding assistant (Claude Code, Cursor, or Codex), with the judgment to raise the whole team's leverage and set how these tools are used well. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Why Join Sensi.AI Sensi is redefining how care organizations support aging at home by using it's Care Intelligence platform to translate everyday signals into meaningful clinical and operational outcomes. Sensi's platform is deployed across 50 states in leading home-care providers, supporting tens of thousands of seniors aging safely at home. By transforming real-world activity in the home into actionable insights, Sensi enables care teams to detect changes in condition earlier, intervene proactively, and deliver higher-quality care at scale. This is a hybrid role based in Tel-Aviv, Israel. About the role As a Senior Data Scientist, you'll use LLMs to turn the outputs of our existing models into richer, more actionable insights. You'll own the work end to end, from defining the problem to evaluating models and shipping to production, and you'll shape how we measure LLM quality across the team. What you'll do Own the LLM lifecycle end to end, from problem framing and model selection through evaluation, deployment, and production monitoring Lead our LLM evaluation: design benchmarks (synthetic and real-world) that measure how commercial and open-source models perform on our actual use cases, and define what "good enough to ship" means Design and build LLM pipelines that turn the outputs of our audio and NLP models into richer, clinically meaningful insights, with a deep focus on prompt engineering and RAG Run experiments and measure their impact in real-world systems, iterating based on data Processing, cleaning, and preparing data from various sources, including unstructured data Partner with product, engineering, and our clinical team to turn real-world needs into AI solutions, and communicate results to technical and non-technical stakeholders Track the state of the art in NLP, LLMs, and agentic AI, and bring what works into our products Requirements 8+ years of hands-on experience in data science, ML engineering, or applied AI roles Hands-on experience building LLM-based solutions in production, including prompt engineering and RAG architectures Proven experience designing LLM evaluations: building benchmarks and eval suites, and using them to compare models and guide decisions Strong Python skills and experience building data pipelines for structured and unstructured data (text, and ideally audio-derived data) A data-driven mindset: you define success metrics up front and iterate based on results Self-directed, comfortable defining the problem as much as solving it Strong communication skills, with the ability to explain complex results to non-technical stakeholders Nice to Have Experience with time series data and forecasting Experience with cloud platforms (AWS preferred) Experience with fine-tuning LLMs Background in healthcare, clinical, or audio-related domains

Description Minute is a global technology and content company built for the future of sports consumption. Minute’s proprietary technology platform enables the creation, distribution, and monetization of digital content experiences, powering Minute’s portfolio of trusted content brands, including Sports Illustrated, The Players' Tribune, and FanSided, as well as hundreds of sports leagues, teams, broadcasters, third-party publishers, and advertisers. The technology platform includes (i) STN Video, an online video platform (OVP) that provides access to a robust sports highlights rights portfolio. Minute is building the future of how the world connects with sport, powered by innovation, shared globally, and trusted by millions of fans and the partners we serve. For more information, visit www.minutegroup.com. The Role: We’re looking for an experienced, rigorous Data Scientist to join our monetization data team in Tel Aviv. You’ll lead dynamic price-floor optimization strategies for programmatic display advertising across our global inventory, build models and experiments that determine which on-page experiences we show each user, and develop strategies to maximize user lifetime value while strengthening engagement and retention. You’ll also shape and manage the roadmap for our internal experimentation platform, enabling rigorous testing across the business. This is publisher-side data science, focused on our own audiences and those of our partners. The bar that matters most here is measurement, proving credibly that a change actually moved revenue before it ships. Responsibilities: • Design and build models for supply-side monetization optimization, user lifetime-value, retention, and ARPU predictions, while measuring revenue impact. • Analyze large-scale event data and maintain metric foundations. • Partner with cross-functional teams to deploy models into production. • Communicate findings and business impact to stakeholders. • Provide technical leadership and mentorship. • Monitor industry trends in programmatic advertising and ad-tech. Requirements • 4+ years in data science / ML applied to a data-heavy, revenue-facing domain - ad-tech, programmatic monetization, marketplaces, pricing, or a comparable real-money optimization problem. • Strong causal-inference and experimentation skills. This is the core of the role. • Fluent SQL on large event datasets and hands-on Python for modeling and analysis. • Solid grounding in the relevant ML families - regression, tree-based models, online optimization / multi-armed & contextual bandits, and classification/segmentation. • In-depth understanding of programmatic advertising from the supply side - RTB, auction mechanics, header bidding, price floors, and how publisher yield is actually made. • Ability to design experiments that measure model effectiveness in production, and to own the definitions (metrics, identity, attribution) they rest on. • Experience integrating models into production systems with engineering, in a fast, iterative environment. • Proven ability to architect and scale models processing high-volume, real-time event streams. • Excellent communication - able to turn a complex monetization result into a clear recommendation a non-technical stakeholder can decide on. • Fluent English, written and spoken. Advantageous: • Hands-on experience with publisher-side monetization/yield optimization, header bidding, or price-floor / seller-side auction optimization specifically. • Familiarity with the Prebid stack (client & server). • Subscription/membership/LTV modeling - retention, churn, and balancing recurring revenue against ad revenue. • Working knowledge of GA4 / behavioral analytics and first-party identity resolution. • Prior experience mentoring or leading a small data-science team. Benefits: • Career development opportunities & different skill-based workshops are offered each quarter. • Gradual return from parental leave. • Fun company events and celebrations. • Community involvement opportunities, both in-person and remote. We're not sure you're 100% qualified, but are you up for the challenge? We want you to apply! Minute is committed to creating a diverse and inclusive work environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Check the URL and email address of any correspondence with Minute. If it is not from @minutegroup.com, treat it as fraudulent. To learn how to protect yourself from recruitment fraud, click here.

Description Stampli is seeking a Senior Data Scientist to drive the development and implementation of AI-based solutions that solve complex business challenges across our innovative SaaS platform. In this key role, you’ll work cross-functionally to lead end-to-end AI projects—from data exploration to deployment—helping shape the future of our intelligent products and user experiences.If you’re passionate about machine learning, LLMs, and real-world impact, and thrive in a collaborative, high-velocity environment, we’d love to connect. What You Will Do • Identify AI opportunities to enhance product experiences and operational efficiency. • Collect, analyze, and model diverse financial datasets to develop actionable AI Products. • Own the full AI solution lifecycle—from problem definition to research, modeling, and hands-on implementation. • Break down complex problems into well-defined, structured work plans. • Collaborate closely with developers, product managers, and other stakeholders to integrate AI solutions seamlessly into products. • Stay up to date with AI, ML, and LLM trends, translating them into practical applications. • Provide technical leadership and mentorship to junior data scientists while balancing immediate delivery with long-term innovation. What You Will Bring Education & Experience • Bachelor's degree in Computer Science, Engineering, or a related STEM field; Master’s in a data science-related field is a plus. • 6+ years in a data science role, ideally within a SaaS or tech-driven company. • Proven experience leading and delivering AI-based solutions to key business problems. • Excellent communication and collaboration abilities (Hebrew & English). Technical Skills • Hands-on experience building and shipping LLM-based agentic systems. • Proven ability to take ML/LLM solutions from research to production, from exploring and prototyping through validation to a production-grade rollout. • Strong evaluation skills: building labeled eval sets, defining metrics, and running offline benchmarks and shadow or A/B comparisons. • Experience deploying and owning models or services in production, including APIs (FastAPI), queue-based async processing, and ongoing monitoring and evaluation. • Ability to investigate production behavior with logs, metrics and data, find the root cause of quality issues, and verify fixes. • Familiarity with cloud services, preferably AWS (S3, DynamoDB, SQS, Bedrock). Advantages • Strong Python engineering: clean, testable code (pytest), async, typed data models (Pydantic), Git and code review. • Experience with retrieval and search: embeddings, vector or hybrid search (OpenSearch/Elasticsearch), and RAG. • Experience with document AI: OCR, layout and table understanding, and invoices or financial documents. • Experience with LLM frameworks such as LangChain or LlamaIndex, and experiment tracking (MLflow). • Experience with big data and data warehouses (Redshift or similar). You Will Get • AI-driven workplace: Work with cutting-edge tools like ChatGPT Enterprise to enhance productivity, spark innovation, and streamline your day-to-day. • Hybrid model: Join us in our Tel Aviv office three days a week (Tue–Thu), with the flexibility to work the rest of the week remotely. • Stock options and real ownership in what you help build. • Career growth supported by experienced, innovation-focused leadership. • Modern, inclusive environment within a fast-moving scale-up culture. Why Join Stampli? Stampli is revolutionizing financial operations with AI, far ahead of competitors. For nearly a decade, our AI assistant, Billy, has set the industry standard—processing $100B+ in invoices annually and saving millions of labor hours for 1,600+ customers. More than automation, Billy transforms finance teams by freeing them from manual work. Our explosive growth places us among the top tech companies at our stage, with exceptionally low churn. After conquering the Accounts Payable space, we are now revolutionizing the entire procure-to-pay (P2P) lifecycle with our new platform, which “connects every dot from request to reconciliation.” What sets us apart? We adapt to our customers—not the other way around—integrating with 70+ ERPs in weeks, not months. Our customer-first approach extends to our award-winning workplace culture and vibrant global offices. We have earned multiple Comparably awards, including Best Company Outlook, Work-Life Balance, Compensation, Happiness, and Perks & Benefits. At Stampli, we build exceptional products with exceptional people. Join our dynamic team and grow your career in an environment that champions creativity, collaboration, ownership, and meaningful innovation.

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships.. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com. BioCatch is looking for a Data Scientist to join our team. The ideal candidate is a highly motivated and self-driven individual with strong analytical and problem-solving skills. The Data Scientist will be responsible for analyzing data and developing models to help inform decisions and strategies for new products and business initiatives. Responsibilities: Work closely with stakeholders to understand business requirements and develop predictive models to detect fraud. Develop and implement statistical models, machine learning and deep learning algorithms to solve complex problems in a production environment. Perform exploratory data analysis and data visualization to gain insights and communicate findings. Collaborate with cross-functional teams, including data engineers and software developers, to design and implement end-to-end solutions that can be deployed in production. Implement advanced analytics tools and technologies to enhance reporting capabilities. Requirements • Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field. • Minimum of 5 years of experience as a Data Scientist or in a related field, with experience working in a production environment or PHD with relevant experience • Strong programming skills in Python and proficiency in SQL. • Deep understanding of statistical analysis, machine learning, deep learning and data visualization techniques. • Strong communication and collaboration skills to work effectively in a team environment. • Skills in developing AI agents, such as LLM-based assistants or autonomous workflows - advantage

About the Role We’re seeking a high-caliber Senior Data Scientist to lead data science initiatives on the Document, Accounting, Takeoffs and Pro Commerce tracks, which are mission-critical aspects of a pro’s project lifecycle. Our mission is to be the thought partners of the Houzz Pro software product team by providing data driven insights to guide product development and business direction. You will work closely with PMs, Engineering, Design and sometimes senior leaders in this role. What You’ll Do • Be the thought partner of the PMs leading the document, accounting, takeoffs and pro commerce tracks to define key metrics, measure and derive insights on how professionals use these features and drive conversations to shape growth oriented product strategies and optimize product roadmap • Lead complex data science initiatives, including sophisticated analyses, independently own the design, running, and analysis A/B tests and other causal inference methods, apply machine learning methods where appropriate and drive adoption of recommendations across teams so we can deliver the best end-to-end document journey, accounting experience, etc. • Dig into large, complex datasets to uncover what drives value for professionals, and translate your findings into clear narratives and dashboards for stakeholders at every level • Work with data engineering to improve data pipelines, instrumentation, and data quality so the team can move fast with confidence • Use AI tools to speed up your own work, from exploratory analysis and code generation to automating recurring reporting, as well as innovative DS projects to uncover new opportunities • Work effectively in areas of significant ambiguity, framing hypotheses, defining success metrics, and leading the implementation of solutions to complex, open-ended problems with minimum guidance, and effectively communicate results to the entire organization including senior leaders • Provide technical guidance to junior team members and foster a culture of continuous learning At a Minimum, We’d Like You to Have • A B.A./B.S. degree in technical/quantitative disciplines or equivalent practical experience • 5+ years experience with analytics, preferably with a SaaS business • Strong proficiency in SQL and Python • A solid foundation in statistics and experimentation, including A/B test design, causal inference, and knowing when a result is truly meaningful • An ability to work independently, run their own projects, host stakeholder meetings, manage expectations of senior leaders and do what is needed to complete the projects with minimal supervision • Strong communication, presentation and interpersonal skills at senior leadership levels • Practical experience working with LLMs, such as calling model APIs, writing and iterating on prompts, or fine-tuning models for specific tasks • Excellent time management and prioritization skills to operate with high autonomy • A product mindset and genuine curiosity about how small businesses and home professionals work • Experience using Tableau Ideally, You'll Also Have • Advanced degree in a technical field such as Statistics, Operations Research or Engineering • Baseline understanding of US business accounting workflows or ERP integrations like QuickBooks; familiar with the home construction, remodeling, or trade business operations such as the concepts of estimating, invoicing, etc. • Familiarity with evaluating generative AI systems, including approaches for measuring output quality, detecting hallucinations, and using LLMs as evaluators Houzz is an Equal Employment Opportunity employer. When applying for a role at Houzz, we guarantee your application will be considered regardless of your sex; race; color; gender; national origin; height or weight; ancestry; physical or mental disability; medical condition; genetic information; marital status; registered domestic partner status; age; sexual orientation; military and veteran status; or any other basis protected by federal, state or local law or ordinance or regulation. We embrace and celebrate the value that diversity brings to an organization. Diverse backgrounds and different points of view help Houzz provide the best experience for our community. Houzz is committed to fostering an inclusive environment through projects and initiatives, such as employee resource groups, that support Houzzers’ efforts to be themselves and share their lives at work. If you would like assistance or an accommodation due to a disability, please email us at [email protected]. This information will be treated as confidential and used only for determining an appropriate accommodation for the interview process. Houzz is an Equal Opportunity Employer. M/F/Disability/Veterans __________________ Be Who You Are and Do What You Love at Houzz About Houzz When founders Adi and Alon remodeled their home, they were frustrated by the lack of resources and inspiration to help them articulate a vision and select the right pro to make it a reality. So they built Houzz. Houzz is now the leading platform for home remodeling and design, providing an all-in-one software solution for industry professionals and tools for homeowners to update their homes from start to finish. Using Houzz, people can find ideas and inspiration, hire professionals, and shop for products. Houzz Pro (houzz.com/pro) provides home industry professionals with a business management and marketing SaaS solution that helps them to win projects, collaborate with clients and teams, and run their business efficiently and profitably. Our Mission and Core Values We're proud to say there's no one quite like us. Houzz is a community-centric, innovative tech company that continues to disrupt the home renovation and design industry. Our mission-driven culture is rooted in our core values, and we’re all here for one purpose: make the home remodeling and design process more fun and productive for everyone. Our Mission To create the best experience for home renovation and design. Our Core Values We’re a Community We put our community of Houzzers, industry professionals and homeowners first. We approach our work with care, humility and respect. We deliver value to our community through our products and services. We Build the Future We are visionaries who challenge the status quo. We are creative, innovative and curious. We embrace change and different ideas to drive our industry forward. We Make Things Happen We are solution-seekers and self-starters. We listen, move fast and empower our teams to deliver extraordinary results and products. We play to win. By applying for a job with us, you acknowledge and agree to the terms of our Job Applicant Privacy Notice. #LI-Remote

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems. Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers. RESPONSIBILITIES • Decide priorities across the functions with their leads, and review the technical trade-offs yourself. • Manage the leads who run these functions and hold each of them to what their team has committed to. • Stay close to production. Own delivery dates, reliability, and what happens when something breaks. • Own the infrastructure for model training, serving, and large scale data processing, including its cost. • Decide which numbers (KPIs) the group is measured on, then track them and report them. • Report the group's progress and risks to senior management, to product, and to the commercial teams. • Work with the applied research team to move their results into production. • Hire and keep senior engineers, and decide how the group is staffed. • Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring. Requirements You'll be a great fit if you have: • 8+ years in engineering, with 5+ years leading teams and managers. • Worked in a multi group environment, leading projects that involve inter group dependencies • Production experience with time series forecasting, reinforcement learning, or large scale optimization. • Experience setting the direction for a machine learning or data engineering group • Enough depth in production ML systems to review architecture decisions with your senior engineers. • Experience holding managers to delivery dates and to the numbers their teams are measured on. • Hands-on familiarity with Python, PyTorch, Google Cloud Platform (GCP), and orchestration frameworks such as Dask and Dagster. • A Bachelor's degree in Computer Science, Engineering, Mathematics, or a relevant field. • The ability to explain a technical decision to a commercial audience, and a commercial constraint to your engineers. NICE TO HAVE • An MSc or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a relevant field. • Experience taking a machine learning platform from an early prototype to something a business runs on. • Experience in airlines, travel, revenue management, or dynamic pricing. • Familiarity with real time inference and high volume data processing. If you want to run the engineering group behind models that set real prices in real markets, we'd like to hear from you.

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. The Large Market Modeling (LMM) team is the engine underneath Fetcherr's pricing intelligence. We're the ones who actually build and train the models — taking a chaotic world of market signals, customer behavior, and competitive dynamics and turning them into reliable, production-ready demand models. Other teams at Fetcherr work with the models; we're the ones who bring them to life. Think of us as the team that teaches Fetcherr's AI how people buy — so it can always recommend the right price at the right moment. We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact. Responsibilities: • Develop and implement state-of-the-art econometric and machine learning models for demand forecasting. • Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability. • Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production. • Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives. Requirements • 5+ years of hands-on experience in data science and machine learning with a proven record of leveraging modeling into business outcomes. • Proficiency in Python and its ML/data stack (e.g., PyTorch or TensorFlow, Pandas, NumPy, Scikit-learn). • Expertise in time-series forecasting, ideally Deep Learning based, preferably in demand prediction or related areas. • Feature engineering, feature importance testing, explainability based experience. • Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering or a relevant field. • Solid understanding of ML production workflows (versioning, testing, reproducibility, and deployment). • Excellent communication and collaboration skills. Nice to have: • Publications in top-tier, peer-reviewed ML/AI venues (e.g. ICLR, ICML, NIPS, etc.) • Experience applying ML in domains like finance, trading, revenue management etc. • Familiarity with cloud based solutions on GCP platform (e.g., Vertex AI, PubSub, Cloud Run Functions). • Strong data visualization and exploratory data analysis skills. • Familiarity with code optimization, containerization (e.g., Docker), CI/CD, or cloud-native architectures. • Participation in competitive programming or data science challenges (e.g., Kaggle). If you're excited about building impactful AI systems in a high-growth startup environment, and want to help redefine how industries price, forecast, and optimize, we’d love to hear from you.

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. We're looking for a Data Scientist to help us grow our advanced analytics team's capabilities. The ideal candidate is a data scientist with a relevant experience who is self-driven, motivated, independent and sharp • Design and operationalize causal-uplift measurement frameworks to quantify the incremental impact of pricing models • Lead data discovery and translate complex multidimensional data into actionable insights related to the pricing algorithms and their outcomes • Conduct research and experimentation to elaborate novel approaches to uplift measuring and a/b testing • Collaborate with revenue managers, data analysts, data engineers in order to encode airline business logic into modeling and technical pipelines At Fetcherr, the Insights & Decision Science (IDS) team is where data meets storytelling. We take the hard work of our AI pricing engine and translate it into something airlines can actually see, trust, and act on — think rigorous statistical analysis with a human touch. If a number moves, we explain why. If a client has a question, we have the answer (and the chart to back it up). Requirements * You have at least 5 years of hands-on data analytics & science experience with proven record of leveraging modeling into business insights * You are proficient in Python (typical data analytics and machine learning stack: pandas, numpy, scikit, statsmodels) * You know to assess the performance and impact of the ML-powered system from business environments perspective * You have great communication and storytelling skills to both technical and non-technical audiences (ability to present to C level stakeholders executives as well) *You are experienced in translating complex client requirements into actionable insights, and ability to present findings and strategies to stakeholders *You are able to provide and recommend innovative solutions and approaches to non-trivial challenges *You’re a team player, ready to help others meeting aggressive timelines and motivate the team to meet the product deadlines *B.SC or Master’s degree in Economy / Statistics / Math or similar (Phd is a plus) You are proficient in using SQL (BigQuery / Dataform / GCP experience is a plus) and git Great if you have them: • Domain knowledge in following industries: airlines, transportation, logistics or vacations • Experience with causal inference, uplift modeling, anomaly detection or pricing algorithms • Knowledge in mathematical optimization

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems. Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers. RESPONSIBILITIES • Decide priorities across the functions with their leads, and review the technical trade-offs yourself. • Manage the leads who run these functions and hold each of them to what their team has committed to. • Stay close to production. Own delivery dates, reliability, and what happens when something breaks. • Own the infrastructure for model training, serving, and large scale data processing, including its cost. • Decide which numbers (KPIs) the group is measured on, then track them and report them. • Report the group's progress and risks to senior management, to product, and to the commercial teams. • Work with the applied research team to move their results into production. • Hire and keep senior engineers, and decide how the group is staffed. • Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring. Requirements You'll be a great fit if you have: • 8+ years in engineering, with 5+ years leading teams and managers. • Worked in a multi group environment, leading projects that involve inter group dependencies • Production experience with time series forecasting, reinforcement learning, or large scale optimization. • Experience setting the direction for a machine learning or data engineering group • Enough depth in production ML systems to review architecture decisions with your senior engineers. • Experience holding managers to delivery dates and to the numbers their teams are measured on. • Hands-on familiarity with Python, PyTorch, Google Cloud Platform (GCP), and orchestration frameworks such as Dask and Dagster. • A Bachelor's degree in Computer Science, Engineering, Mathematics, or a relevant field. • The ability to explain a technical decision to a commercial audience, and a commercial constraint to your engineers. NICE TO HAVE • An MSc or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a relevant field. • Experience taking a machine learning platform from an early prototype to something a business runs on. • Experience in airlines, travel, revenue management, or dynamic pricing. • Familiarity with real time inference and high volume data processing. If you want to run the engineering group behind models that set real prices in real markets, we'd like to hear from you.

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data scientists & engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products. The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions — like demand forecasts — to drive revenue management decisions that actually move the needle. At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through a price optimization decision model that simulates the market — weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies — to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything. You will lead the team through architecture decisions, development, and deployment of mission-critical systems—while growing and mentoring a high-performing team. Responsibilities: • Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure. • Analyzing existing models and algorithms, researching for new approaches in price optimization. • Design, build and maintain distributed data processing workflows (batch & streaming). • Drive best practices for data quality, validation, testing, and observability. • Own and evolve Fetcherr’s data architecture in alignment with business and product goals. • Manage sprint planning, task breakdown, code reviews, and performance feedback for your team. • Contribute hands-on to key development tasks and architecture decisions. • Recruit, mentor, and grow the data science engineering team. Requirements • 3+ years of proven experience leading and managing a team of data science / data engineers (people management is required). • 4+ years of experience in data science, including a strong Python programming background, advanced SQL skills, and data modeling experience. • Proven experience designing and maintaining large-scale data platforms. • Expertise with data orchestration tools (Airflow, Prefect, or Dagster) • Cloud platform experience — GCP preferred (AWS/Azure acceptable) • Strong communication, mentorship, and collaboration skills • BSc/MSc in Mathematics/ physics/ statistics/ Computer Science. • Fluent English (spoken and written) Nice to have: • Experience with Google Cloud Platform tools (BigQuery, Dataflow, Pub/Sub) • Background working with ML pipelines or data science teams • Understanding of data contracts, lineage, and versioning tools • Knowledge of data structures and algorithms • Familiarity with Docker • Experience optimizing pipelines and databases for performance and cost • CI/CD pipelines (GitHub/GitLab) • Hands-on experience with distributed data frameworks (Apache Beam, Spark, or similar) • Exposure to airline, pricing, or GDS systems

Description Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. The Large Market Modeling (LMM) team is the engine underneath Fetcherr's pricing intelligence. We're the ones who actually build and train the models — taking a chaotic world of market signals, customer behavior, and competitive dynamics and turning them into reliable, production-ready demand models. Other teams at Fetcherr work with the models; we're the ones who bring them to life. Think of us as the team that teaches Fetcherr's AI how people buy — so it can always recommend the right price at the right moment. We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact. Responsibilities: • Develop and implement state-of-the-art econometric and machine learning models for demand forecasting. • Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability. • Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production. • Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives. Requirements • 5+ years of hands-on experience in data science and machine learning with a proven record of leveraging modeling into business outcomes. • Proficiency in Python and its ML/data stack (e.g., PyTorch or TensorFlow, Pandas, NumPy, Scikit-learn). • Expertise in time-series forecasting, ideally Deep Learning based, preferably in demand prediction or related areas. • Feature engineering, feature importance testing, explainability based experience. • Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering or a relevant field. • Solid understanding of ML production workflows (versioning, testing, reproducibility, and deployment). • Excellent communication and collaboration skills. Nice to have: • Publications in top-tier, peer-reviewed ML/AI venues (e.g. ICLR, ICML, NIPS, etc.) • Experience applying ML in domains like finance, trading, revenue management etc. • Familiarity with cloud based solutions on GCP platform (e.g., Vertex AI, PubSub, Cloud Run Functions). • Strong data visualization and exploratory data analysis skills. • Familiarity with code optimization, containerization (e.g., Docker), CI/CD, or cloud-native architectures. • Participation in competitive programming or data science challenges (e.g., Kaggle). If you're excited about building impactful AI systems in a high-growth startup environment, and want to help redefine how industries price, forecast, and optimize, we’d love to hear from you.

Description Fetcherr builds responsible AI that turns market complexity into measurable profit growth. At its core is the Market Model, a proprietary AI model that delivers granular demand predictions and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data, not personal data, with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic, and Fetcherr delivers a consistent average profit uplift of 7%. Fetcherr Labs is a new unit that takes this engine to mid-size companies, run by agents and delivered inside the tools customers already use, such as Excel and Copilot. Labs is small, flat and senior, and builds with AI agents from day one. You will work directly with the Market Model Lead and report to the Chief of Labs. The role We are looking for a Demand Modeling Scientist who owns demand models end to end: from a new customer's raw history to a model running in production, measured against real outcomes. You will teach the engine how demand behaves in new industries, starting with aftermarket parts, and prove the uplift through live A/B tests with our design partners. What you'll do • Develop and implement state-of-the-art econometric and machine learning models for demand forecasting. • Own the full machine learning lifecycle: data exploration, feature engineering, training, validation, deployment, monitoring, retraining and retirement. • Conduct research and experimentation to improve accuracy, robustness and scalability, and adapt models to new industries and customer data. • Design how models are measured in production, including A/B tests that show the uplift for each customer. • Build with AI coding agents as your everyday way of working: agents write and test much of the code, and you design, review and decide. • Work with data and platform engineers to put models into production on Azure, and keep them explainable and reliable. • Explain complex findings clearly to non-technical stakeholders, including customers and executives. Requirements What you bring • 5+ years of hands-on experience in data science and machine learning, with a proven record of turning models into business outcomes. • Proven experience taking models through the full lifecycle into production: versioning, testing, reproducibility, deployment, monitoring and retraining. • Expertise in time-series forecasting, ideally deep-learning based, preferably in demand prediction or related areas. • Feature engineering, feature importance testing and explainability experience. • Proficiency in Python and its ML and data stack (PyTorch or TensorFlow, Pandas or Polars, NumPy, Scikit-learn, SQL). • Hands-on use of AI coding agents (such as Claude Code or GitHub Copilot) in your daily development, and a strong view on where they help and where they don't. • Master's or PhD in Computer Science, Machine Learning, Statistics, Engineering or a related field. • Excellent communication and collaboration skills. Nice to have • Experience with pricing, revenue management, retail or B2B distribution. • Experience with experimentation and causal measurement (A/B testing, uplift). • Experience with cloud ML platforms (Azure ML or Microsoft Fabric preferred; GCP or AWS also relevant). • Familiarity with workflow orchestration in general (any modern orchestrator). • Publications in top-tier peer-reviewed ML venues (ICLR, ICML, NeurIPS) or strong results in data science competitions. If you want to own demand models end to end, build with agents, and bring a proven AI engine to a whole new market, we'd love to hear from you.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io About the Role We are seeking a visionary and hands-on AI and Data Science Team Lead to head our advanced analytics and AI engineering group. Over the past 7 years, our data infrastructure and customer needs have evolved from foundational data engineering and cloud migrations to cutting-edge Generative AI and Machine Learning solutions. You will lead a high-performing team of AI Engineers and Data Scientists, taking ownership of the technical direction, architecture, and deployment of production-grade AI systems on AWS. The ideal candidate combines a solid foundational background in complex data architecture with state-of-the-art expertise in Gen AI, RAG pipelines, graph-based reasoning, and Enterprise MLOps. You will work closely with enterprise customers across various sectors (manufacturing, banking, healthcare, and high-tech) to translate business constraints into scalable architectures and ship systems that deliver measurable business impact. Responsibilities • Team Leadership: Recruit, mentor, and manage a team of Data Scientists and AI Engineers, driving engineering excellence, technical direction, and best practices. • System Architecture & Gen AI: Design and deploy end-to-end AI systems, including large-scale Retrieval-Augmented Generation (RAG) pipelines for technical documentation, Text-to-SQL GenBI agents, and advanced AI document processing systems (OCR, semantic extraction). • Enterprise MLOps: Design and deliver secure, enterprise-grade MLOps platforms on AWS (including CI/CD pipelines, feature stores, and monitoring) to dramatically reduce model deployment times. • Advanced Data Modeling: Utilize graph embeddings (e.g., Neo4j) and build graph-based AI agents to uncover hidden behavioral patterns, improving LLM reasoning while optimizing token costs and latency. • Customer & Stakeholder Collaboration: Work directly with enterprise customers to understand business constraints, define architectural standards, and implement governance guardrails and validation layers for safe enterprise adoption and data protection. • Hands-On Execution: Operate dynamically in both startup and enterprise contexts, balancing the need to ship value quickly with maintaining strict production rigor. Requirements • Experience: 7+ years of proven experience designing, deploying, and managing production-grade Machine Learning and AI systems (with a strong foundation in data engineering and scalable pipelines). • Leadership: Demonstrated experience leading, managing, and mentoring data science or AI engineering teams. • Cloud & Infrastructure: Deep architectural expertise in the Amazon Web Services (AWS) ecosystem, with a track record of deploying large-scale solutions. Experience defining architectural standards to achieve milestones like the AWS ML Competency is a significant advantage. • Gen AI & Search: Extensive hands-on experience with Large Language Models (LLMs), deep learning frameworks, domain-specific prompting, and vector search/Elasticsearch. • Graph Technologies: Strong background in modeling complex data structures using Graph Databases (e.g., Neo4j) to build scalable pipelines for high-volume data. • Education: M.S. (or equivalent) in Engineering, Mathematics, Computer Science, or a related quantitative field. • Communication: Excellent interpersonal skills with the ability to lead customer-facing engagements and translate complex technical concepts into business value. • Fluent in Hebrew and English AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics, or any other basis forbidden under federal, provincial, or local law.

Description Upstream delivers a cloud-based, AI-powered behavioral intelligence platform for physical AI and smart mobility. Upstream empowers customers with advanced AI-driven cybersecurity solutions, including detection and response (XDR), AI Security, quality and operational intelligence, cyber threat intel, SOC services, and more. Upstream is looking for an experienced Senior Data Scientist to join our rapidly growing data science team, focusing on creating prediction and anomaly detection models. As a Senior Data Scientist, you will lead the analysis of large (often massive) datasets, develop models and algorithms to meet customer needs, and work closely with other data scientists and data engineers to improve our automotive purpose-built data management platform, focusing on cybersecurity, API security, and vehicle quality use cases. This role is full-time and is Herzliya, Israel based. Responsibilities • Conduct in-depth research on large and diverse automotive live streaming tabular datasets • Explore, design and deploy cutting-edge models or algorithms solutions for prediction purposes and anomaly detection • Collaborate closely with domain experts and peers to research and implement models and algorithm flows that best fit requirements • Partner with the data engineering and development teams to devise innovative solutions for productization • Encourage collaboration and knowledge-sharing within the data science team Requirements • 5+ years of relevant industry experience in data science or machine learning • Education: MSc in Math / EE / CS / Statistics • Demonstrated research expertise in the tech industry or published academic papers • Profound understanding and hands-on expertise in mathematics, statistics, machine/deep learning, and data mining • Expertise in data science pipelines is essential, including data cleansing, feature engineering, and model tuning • Coding and software engineering experience focusing on advanced proficiency in Python, especially in data science libraries such as Pandas, Polars, NumPy, SciPy, Scikit-learn, and others • End-to-end experience in leading the development of AI/DS/ML products or features • Demonstrated experience in implementing algorithms/models in a production environment, whether in batch processing or real-time scenarios • Proficiency in SQL Experience in deep learning, utilizing platforms such as TensorFlow, Keras, PyTorch, etc. - Advantage • Strong communication skills, with the ability to convey ideas and share knowledge effectively • Independent and quick learner, capable of developing independently • A valuable team player who cooperates well • Experience thriving in a dynamic work environment Upstream is an equal opportunity employer. All candidates for employment will be considered without regard to race, color, religion, sex, national origin, physical or mental disability, veteran status, or any other basis protected by applicable federal, state or local law.

JLL empowers you to shape a brighter way. Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. Senior Data Scientist (Hybrid) – Tel Aviv About us JLL (NYSE: JLL) is a Fortune 200 company and a global leader in commercial real estate. We are a $20B+ business with over 100,000 employees worldwide and the stability and resources to invest seriously in AI. Under our Accelerate 2030 strategy, data and AI are the core pillars of how the company will operate and grow. The Tel Aviv technology hub is at the center of that effort. We design, build, and operate production-grade AI systems that shape how decisions are made across JLL's diverse businesses. The position We are looking for a Senior Data Scientist to join the Enterprise Data Science Group — an outstanding data scientist who thrives on complex problems that require critical thinking and rigorous evaluation, the kind that can’t be solved out of the box with vibe coding. You’ll code solutions end-to-end and shape how machine learning and AI drive business value at scale. You'll work across the full stack: dozens of data sources, billions of data points, closed and open-weight LLMs, fine-tuning LLMs, agentic workflows, and multiple cloud environments. Most of your projects go past research and into production, where the rest of the business depends on them. Representative projects: • GenAI-based entity resolution, linking companies and properties across dozens of sources using custom embeddings, LLMs at scale, fine-tuning, and clustering. • AI-generated global company taxonomies that rival purchased third-party solutions, across millions of companies and their domestic and global hierarchies. • Agentic, LLM-based, and ML-driven recommender systems for driving cross-sell and up-sell across our businesses. • Forecasting commercial lease rent dynamics and market trends from millions of leases and JLL's global transaction data. • Agents and MCP servers that put our research and data to work, plus exploratory tracks on tabular and time-series foundation models, multi-agent pipelines, reinforcement learning, and novel evaluation. We are looking for • 5+ years of industry experience, with original research and/or ML/AI solutions deployed in production. • Sound judgment in choosing the approach for the situation: when to use frontier model APIs, fine-tuned open-weight models, agentic workflows, classical ML, etc. • Strong foundations in ML and statistics. • A solid understanding of transformers and LLM internals, from attention, tokenization, and embeddings to how models are post-trained and served. • A rigorous evaluation mindset across classical and non-deterministic systems: error analysis, benchmark creation and ground-truth design, LLM-as-judge with human calibration. • Hands-on experience with agentic systems: tools and MCP design, RAG, multi-agent orchestration, and context engineering. • An MSc (PhD an advantage) in computer science, mathematics, or another quantitative field, or equivalent experience. Ways to stand out • Experience post-training open-weight models (e.g. Llama/Qwen-class): SFT, preference optimization and RL (DPO, GRPO, RLVR), LoRA vs. full fine-tuning, and synthetic data curation. • Hands-on experience scaling LLMs in training and inference: distributed training (FSDP, DeepSpeed), high-throughput serving (vLLM, SGLang), quantization (FP8/INT4), and KV-cache optimization. • Engineering with data at scale: pipelines, production deployment, and owning cost, latency, and monitoring in the cloud. • A public research footprint including publications, technical blogs, or conference talks. We're a tight-knit team of about 20 with a strong, fun, collaborative culture and a positive atmosphere, hybrid from our newly renovated Tel Aviv headquarters, a homelike space with team breakfasts and an on-site gym. We offer highly competitive compensation and benefits, including private health insurance. Location: On-site –TEL AVIV, ISR If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements. We’re interested in getting to know you and what you bring to the table! At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you're pursuing. JLL Privacy Notice Jones Lang LaSalle (JLL), together with its subsidiaries and affiliates, is a leading global provider of real estate and investment management services. We take our responsibility to protect the personal information provided to us seriously. Generally the personal information we collect from you are for the purposes of processing in connection with JLL’s recruitment process. 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Description We are hiring a Principal Data Scientist for the Data Science group, reporting directly to the VP Engineering. This is a senior individual-contributor leadership role: you own the technical direction and architecture of what the group ships - real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath - and you lead the hardest projects end to end, hands on in the code. What you'll own A large Python, Rust, and PySpark monorepo, dozens of production services and shared libraries, across three connected domains: Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through our in-house ActiveServe framework over NVIDIA Triton and a Rust detection service, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. GenAI safety and red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives, and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok). Data platform and MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do • Own the GenAI safety roadmap - decide what we build with LLMs and what we build around them: guardrails and LLM-as-judge adjudication, prompt-injection and jailbreak detection, policy authoring, and the automated red-teaming that measures attack-success rate against customers' applications. Choose which model families and providers we use, fine-tune, or retire, and what each has to prove before it reaches customer traffic. • Own how we evaluate GenAI systems - benchmark and dataset design over real production traffic, LLM-jury labeling protocols, multilingual, multi-turn, and multimodal coverage, calibration and threshold selection, and the error analysis that turns a metric into a fix. Run the evaluations that separate a real shift in what these models can do from a launch post, and keep quality honest in production as traffic, prompts, and attacks drift. • Lead the hardest GenAI work end to end, and grow the craft - take the ambiguous, high-stakes projects from framing through prototype, offline evaluation, shadow traffic, and rollout; mentor data scientists and ML engineers through design, experiment, and model review; and translate model behavior into what Product and customers care about: accuracy, latency, cost, and enterprise readiness. Requirements What we're looking for • ML technical leadership - a proven tech lead, senior data scientist, or ML engineer who has taken multi-person, multi-quarter ML initiatives from framing to production without formal authority, with strong Python and the written communication to make the case in a design or an evaluation. • Deep understanding of LLM technology - how these models are trained, adapted, and served, what prompting, fine-tuning, RAG, and agentic patterns each buy you, where they fail, and how to measure quality, latency, and cost. • AI-augmented engineering - deep, daily fluency with an AI coding assistant (Claude Code, Cursor, or Codex), with the judgment to raise the whole team's leverage and set how these tools are used well. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!