Data Scientist jobs in Tel Aviv-Yafo

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.

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

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 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.

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. We endeavour to keep your personal information secure with appropriate level of security and keep for as long as we need it for legitimate business or legal reasons. We will then delete it safely and securely. For more information about how JLL processes your personal data, please view our Candidate Privacy Statement. For additional details please see our career site pages for each country. Jones Lang LaSalle (“JLL”) is an Equal Opportunity Employer and is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process – including the online application and/or overall selection process – you may email us at [email protected]. This email is only to request an accommodation. Please direct any other general recruiting inquiries to our Contact Us page > I want to work for JLL.

Description Sentra is the global leader in cloud-native data security for the AI era. The company’s mission is to empower organizations to confidently scale their data operations across multi-cloud and on-premises environments while leveraging the power of AI without compromising security. Sentra’s unique approach enables enterprises to autonomously scan their environments without the need for agents, ensuring that data remains securely within their cloud or on-premises infrastructure. This innovative methodology sets us apart in the industry, providing organizations with control and visibility over their sensitive data at all times. Our commitment to excellence in data security posture management and data detection and response makes Sentra a leader in the field. We are seeking an experienced Data Science Team Lead to join us and play a key role in shaping the future of data security. As a Data Science Lead, you will be part of Sentra’s Classification team, which lies at the core of our mission - identifying sensitive data and helping customers detect the most critical risks in their cloud environments. Our multidisciplinary team of data scientists. Responsibilities • Lead the end-to-end lifecycle (design, development, and deployment) of ML models (e.g., NER, LLMs) to advance our data classification and entity recognition capabilities. • Partner with Product to align cutting-edge AI technologies with real-world customer needs and Sentra’s roadmap. • Architect robust experimentation frameworks to accelerate model iteration, validation, and production deployment. • Utilize extensive datasets to define quality benchmarks and drive continuous performance improvements. • Champion innovation by identifying opportunities for automation and AI-driven features that maximize customer value. Requirements • 7+ years of DS experience, with deep expertise in NLP and/or Generative AI. • 3+ years leading DS teams (managing 3+ ICs), with a focus on team growth and mentorship. • Proven track record of scaling ML models in production environments. • Strong proficiency in Python, SQL, and modern ML frameworks. • Hands-on experience with Big Data technologies, MLOps, and cloud-based AI infrastructure. • Strategic mindset, with experience managing technical debt and defining long-term research roadmaps in Agile environments. • Excellent communication skills, capable of translating complex technical insights for diverse stakeholders. Nice to have: • Prior experience in the data security or the compliance industry. • Experience processing large amounts of data using Big Data tools like Spark. • Experience using cloud based fine tuning frameworks. • A Master’s or Ph.D. in a relevant field (e.g., Data Science, Computer Science, Statistics, Mathematics) is preferred.

Description Join Personetics as an Intermediate Applied Data Scientist, where you'll work with a dynamic team to analyze complex data and deliver innovative solutions for our clients. Utilize your expertise to drive data-driven decision-making and support the development of cutting-edge financial technology. About the position We are hiring an Applied Data Scientist to build the models that turn raw transaction data into the insights our banks’ customers see every day. The stack is deliberately wide: classical ML on tabular data, deep learning, LLM-based systems, and agentic flows. We choose the right approach for each problem, balancing analytical quality, production readiness, and time to market. You drive the analytical work, working closely with Product and Engineering to take ideas from evidence through production. Responsibilities • Analyze and interpret complex datasets to identify trends and patterns. • Collaborate with cross-functional teams to develop data-driven solutions. • Build and maintain predictive models to enhance business decisions. • Communicate findings and insights effectively to stakeholders. • Stay updated with the latest data science technologies and methodologies. • Contribute to the continuous improvement of data processes and systems. Requirements • Bachelor's degree in Data Science, Computer Science, or related field. • 3+ years of experience in applied data science or a similar role. • Proven experience with machine learning algorithms and statistical modeling. • Proficiency in programming languages such as Python or R. • Strong analytical skills and ability to work with large datasets. • Excellent communication and teamwork skills. • Experience with data visualization tools like Tableau or Power BI. Nice to have • FinTech or banking experience. • Transformer models — fine-tuning and production inference. • Experience taking a model into production, including monitoring and post-launch iteration. • Cloud platforms (AWS, Azure).

Description About Us Arpeely is bootstrapped and has been profitable since day one. We built one of the most efficient programmatic acquisition engines on the planet: processing billions of ad decisions a day, generating terabytes of live behavioral data, and turning it into margins most SaaS companies would kill for. At the core of that engine is a team of data scientists and ML engineers running tens of billions of sub-second predictions a day, deciding in milliseconds which impression is worth buying and what it’s worth. Now we’re taking everything we’ve learned about how people move through the web and putting it into our most innovative consumer product yet: Ace AI, an AI-native browser already reaching millions of people, built to simplify and improve everyday online life with the help of AI. This is a rare opportunity: a category that hasn’t meaningfully changed in over a decade, and a profitable company with the data and the acquisition engine to win it on its own terms. Come build it with us. What You’ll Do • Mentor a team of extremely talented and success-hungry data scientists. • Initiate and manage cutting-edge projects from inception to delivery, including developing structured and automated solutions. • Lead the data strategy: be responsible for planning, prioritizing, and building the right data solutions to support business needs. • Apply your scientific knowledge and creativity to analyze large volumes of diverse data and develop algorithmic solutions and models to solve complex problems. • Identify and propose data science and algorithmic solutions for various product initiatives. • Use your excellent communication skills, both written and verbal, to explain technical concepts and analysis implications to senior leaders, and influence cross-functional teams to improve performance. • Partner with other departments (e.g. Management, R&D, Product) to support decision making. Requirements • Two years of experience in leading a team. • 4+ years of practical analytics experience in big data businesses (preferably at a B2C company). • A hands-on, passionate person who likes to get to the bottom of things in terms of algorithms, data, and business. • Excellent analytical skills with experience in querying large, complex data sets. • Strong understanding of statistical significance. • Hands-on experience with machine learning algorithms; Python and data visualization tools such as Tableau, Looker, etc. • BSc degree (or higher) in Mathematics, Statistics, Engineering, Computer Science, or any other quantitative field. • Self-learner, multi-tasker, able to work independently. • Self-sufficient, proactive problem-solver. • Excellent communication skills, both written and verbal. • Familiarity with the ad-tech industry — a big plus. • Experience in implementing real-time machine learning and data mining algorithms in large-scale environments — a big plus. • Fun to work with and a team player :)

About Payoneer Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world’s underserved businesses to a rising global economy. We’re a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories. By taking the complexity out of the financial workflows–including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence–we give businesses the tools they need to work efficiently worldwide and grow with confidence. Senior Data Scientist – AI & ML Hybrid Full-time Role Summary You’ll be one of the founding data scientists on our brand-new ML team, building Payoneer’s AI and data science capabilities from the ground up. Working closely with the Tech Lead, you’ll develop and deploy models across payments, risk and customer analytics, and build the frameworks that evaluate our GenAI and LLM-based agents. You’ll set the modeling standards, technical practices and culture the team will scale on, and help hire the data scientists who come next. It’s a great fit if you’re excited to build something new and own it end to end. What You’ll Do • Build evaluation frameworks for GenAI and LLM agents – golden datasets, LLM-as-judge, and quality, safety and compliance monitoring • Own the full ML model lifecycle, from research and experimentation through production deployment and monitoring • Design and deploy ML models that solve complex business problems across payments, risk and customer analytics • Work with the Tech Lead to define core modeling methodologies, analytical standards and research practices • Set up the team’s approach to feature engineering, model validation, experimentation, A/B testing and performance monitoring • Lead exploratory research to uncover insights, identify ML opportunities and evaluate new techniques • Partner with business stakeholders to translate problems into data science solutions and communicate findings clearly • Shape the team’s culture and documentation standards, and help interview, onboard and mentor new data scientists Who You Are • 5+ years of experience in Machine Learning, Data Science or a related technical role • Proven track record of building and deploying ML models in production environments • GenAI/LLM evaluation experience – benchmark datasets, LLM-as-judge or human-in-the-loop evaluation, and monitoring of RAG and agent systems • Strong proficiency in Python and SQL, and in ML frameworks such as TensorFlow, PyTorch and Scikit-learn • Experience with cloud platforms (AWS, GCP or Azure) and MLOps tools and practices • Technology leadership – a history of making tech stack decisions and establishing development practices that scale • Excellent communication skills, with the ability to explain technical concepts and influence technical decisions • Entrepreneurial, self-starter mindset – excited to build from scratch and comfortable with ambiguity and ownership • Sc. or M.Sc. in Computer Science, AI, Data Science, Mathematics or a related quantitative field • Experience in fintech or financial services – an advantage The Payoneer Ways of Working Act as our customer’s partner on the inside Learning what they need and creating what will help them go further. Do it. Own it. Being fearlessly accountable in everything we do. Continuously improve Always striving for a higher standard than our last. Build each other up Helping each other grow, as professionals and people. If this sounds like a business, a community, and a mission you want to be part of, apply today. We are committed to providing a diverse and inclusive workplace. Payoneer is an equal opportunity employer, and all qualified applicants will receive consideration for employment no matter your race, color, ancestry, religion, sex, sexual orientation, gender identity, national origin, age, disability status, protected veteran status, or any other characteristic protected by law. If you require reasonable accommodation at any stage of the hiring process, please speak to the recruiter managing the role for any adjustments. Decisions about requests for reasonable accommodation are made on a case-by-case basis.