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.

Vi is an enterprise AI platform for health enterprises – healthcare, biopharma, and wellness. We deploy agentic AI and predictive models into production environments where the output drives the next best actions for patients, care teams, and operations to deliver ROI and improve health outcomes. The Vi Platform is the shared layer under every Vi application. It turns data, models, agents and compliance into services that every product uses to onboard clients, reach patients and measure results, so a capability is built once and reused everywhere. We are looking for a senior data scientist to take those services from idea to production, and to keep them running as they grow. You will be part of the Vi Platform team, reporting to the VP of R&D. This is a hands-on IC role at the crossroads of data science, software engineering and cloud infrastructure: you design the service, you build every layer of it, you ship it, you measure what it does for the products that use it, and you maintain and extend it with the features its users need. Responsibilities Train and evaluate models on tabular data: Turn patient- and member-level tabular data – claims, clinical, engagement and vendor feeds – into features, labels and models (gradient boosting, tabular foundation models, embeddings), with an evaluation harness that defines what "good" means before a product feels a change. Own services end to end: From a one-page design through API, SDK, UI and infrastructure to production, built to the platform's service standards: tested at every level, auto-deployed on merge, with live status and cost per job. Build data services at scale: Spark and EMR jobs over the lakehouse, orchestrated in Airflow, that resolve identities, join vendor feeds and score millions of records per run, and that get faster and cheaper with every release. Put models and LLMs into production: Foundation model, embedding, classification and agent services, each with an evaluation harness behind it, so a model change is measured before a product feels it. Build the full stack: Backend services, web consoles and SDKs that an engineer, a product team or an agent can use self-serve, with a user guide for people and a playbook for agents. Run what you ship: Monitoring, alerting, retries and cost per job. You are the first responder when your service is red, and you announce every release to its users with measured numbers. Work AI-native: AI coding agents are part of how we build every day. You drive them, review their output critically, and build services that agents consume as easily as people do. Requirements 4+ years of hands-on data science experience, with a track record of training, evaluating and shipping models that run in production as a hands-on IC. Data science and ML in production: Building and evaluating models (tabular, embeddings, LLM-based), deciding what "good" means for a service, and measuring it. Data engineering at scale: Python, SQL and Spark on AWS (EMR, Athena, Glue, Iceberg), workflow orchestration (Airflow), and performance work on large joins and feeds. Tech Stack: AWS, Docker, CI/CD, and monitoring; you own the infrastructure of your service, not just the code. Agentic workflows: Designing, shipping and evaluating multi-step LLM agents that call tools, models and data services to get real work done, with evals that catch regressions before users do, and an AI-first way of working with coding agents as a daily tool. High ownership and velocity: You scope, design, build, ship and support without a hand-off. You write the design doc and the user guide, and your work stands up to a review.

Play Perfect is a fast-growing mobile gaming company that provides entertainment to millions of users around the world. The company is headquartered in Tel Aviv. With a team of talented and passionate developers, designers, industry experts and gamers, Play Perfect is at the forefront of creating immersive gaming experiences that engage players on a whole new level. As we continue to push the boundaries of what is possible in gaming, Play Perfect is poised to shape the future of the industry and redefine what it means to be a skill-based gaming company. As we expand, we're looking for a hands-on Data Scientist to own research, experimentation, and simulation. You'll independently drive projects from business question to product recommendation, combining strong statistical skills, creative thinking, and fast execution. Working closely with Product, Development, Game Economy, and BI, you'll help shape game mechanics and improve player and business outcomes. Responsibilities: Own data science projects end to end, from defining business questions and success criteria to building, deploying, and maintaining solutions that deliver actionable insights and business impact. Analyze and optimize game economies, balancing player experience, engagement, and business performance. Design and evaluate reward systems, personalization mechanisms, and game features using statistical modeling and simulations. Lead A/B testing initiatives, including experiment design, analysis, and interpretation of results, and develop reliable experimentation tools. Apply statistical and causal inference methods to understand player behavior, evaluate product changes, and support decision-making. Develop and maintain AI-powered tools and solutions to improve business processes and team efficiency. Communicate findings clearly to technical and business stakeholders, document methods and decisions, and collaborate with cross-functional Requirements 3+ years of experience in Data Science, Applied Statistics, or a similar analytical role. B.Sc. or higher in Statistics, Mathematics, Computer Science, or a related quantitative field. Strong statistical foundation, including experimental design, hypothesis testing, and A/B testing. Experience with simulations, probabilistic modeling, and data-driven decision-making. Strong Python and SQL skills for data analysis, modeling, and development. Ability to independently lead projects end to end, with strong problem-solving and communication skills. Experience with causal inference, machine learning, or game economy – an advantage. Experience building AI agents or deploying analytical applications – an advantage.

Istra is a leading Algo-Trading research company specializing in developing and deploying High Frequency Trading strategies (HFT). We use state of the art technology and algorithms that are developed by our elite highly-skilled group of experienced researchers and developers in a challenging, pleasant, and rewarding work environment.We are always on the lookout for outstanding data scientists to join our team.Our researchers explore phenomena in the financial markets and are responsible for researching new trading strategies as well as analyzing and improving the performance of existing algorithms.In this role, you will:Analyze large and complex data sets to identify patterns and trends.Develop and improve algorithms for trading strategies.Collaborate with the team of researchers and developers in the development and deployment of new trading strategies.Explore new algorithms in the fields of machine learning, signal processing, data mining and statistics.Participate in knowledge sharing and continuous learning opportunities. Requirements What you'll need to succeed:Summa cum laude BSc. in Mathematics, Physics, Computer Science or Electrical Engineering from a well-known academic instituteAdvanced degree (MSc./PhD.) in a relevant field is a plusProgramming experience (C++ - advantage)Experience in statistical analysis of large data sets – significant advantageDeep learning experience – advantageExperience in quantitative finance – advantageA deep sense of intellectual curiosity and excellenceIndependent, thorough, motivated, and innovativeSo, if you enjoy working with excellent people while analyzing huge data sets and have interest in delving into and extracting the essence out of them, we would love to hear from you.

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 Applied Data Scientist — Intent Drift Detection Backslash Security · Realtime AI-agent protection About Backslash We are builders who want to secure the next generation of software development from the inside out. Software development has entered a new era defined by vibe-coding, MCP servers, and AI agents. While these tools allow developers to build faster than ever, they also introduce a completely new frontier of security challenges that exist directly on the engineer's machine. Backslash is building the security layer for this new reality. We provide the essential guardrails and visibility needed to secure AI-driven workflows. By actively protecting developer computers and monitoring IDE-based AI interactions, we ensure the future of coding is as secure as it is fast. The Role AI coding agents run with enormous privilege on developer machines. When untrusted content steers one off its task, classical security sees only the final action — never that the agent's intent drifted. Detecting that drift, in realtime, on-device, is an open problem. No one has solved it. You will. You will own intent-drift detection end-to-end — from the data to the model: • Build the Dataset: Design and generate the data that defines what "drift" looks like, and co-design the pipeline that creates it. • Train Small, Ship Local: Fine-tune, distill, and shrink the models that detect drift in realtime — small enough to run fully on-device, no cloud. • Shape the Architecture: Decide what's a model, what's a rule, and what's a gate. The detector's design is your call. • Measure What Matters: Build the evaluation harness — realtime detection at very low false-positive rates on a developer's machine is the product. You own intent-drift analysis as a problem, not a ticket. Requirements • Applied Data Science End-to-End: 3–5 years turning ambiguous problems into working models — problem framing, data collection and labeling strategy, feature/representation design, training, and evaluation. You've owned a dataset and the model that consumed it. • Small-Model Fluency: Comfortable fine-tuning small open models locally — DeBERTa-class encoders, ≤8B decoders, LoRA, quantization, single-GPU workflows. • Applied-Research Mindset: You approach an open research problem with passion and a practical, ship-it attitude. • Independence: You want an impactful, strategic problem in LLM security — not a ticket queue. • Startup DNA: Can-do, fast, a true partner. Non-negotiable. Local-first by design. Python, PyTorch, HuggingFace, ≤8B models. Bonus Points For • Security Background: Prompt injection, MITRE ATLAS, agent security — or the hunger to learn it fast. • Adversarial ML: Experience with LLM evaluation, red-teaming, or building adversarial datasets. • Endpoint Software: Familiarity with software that runs locally on developer machines with a low resource footprint.

We're looking for a Senior Data Scientist who can translate ambiguous product questions into measurable experiments, robust models, and data‑driven strategy. You'll partner with engineering, product and design to unlock insights, prototype and ship machine‑learning solutions, and define the metrics that steer Genway's AI‑powered user‑research platform. Candidates should bring 5 + years of hands‑on experience deploying production ML/AI systems, leading A/B tests at scale, and mentoring others in a fast‑moving start‑up environment. Experience with generative AI or real‑time streaming data is a major advantage. Personal Requirements Bias for action & ownership - you're comfortable owning analyses end‑to‑end and influencing product roadmaps with data‑backed recommendations Curiosity & scientific rigor - formulate hypotheses, design experiments and measure impact with statistical discipline Strong communicator & team player – explain complex models to cross‑functional stakeholders and collaborate closely with engineers, PMs and designers Startup experience – thrive amid changing priorities and ship increments quickly to deliver business value Technical Requirements 5 + years building & deploying ML/AI models in Python (pandas, scikit‑learn, TensorFlow/PyTorch) Expert SQL skills plus familiarity with modern cloud data warehouses (BigQuery, Snowflake, Redshift) Statistical experimentation & causal inference - design, power and analyse A/B & multivariate tests Production‑grade data pipelines & MLOps - orchestrate batch/stream processing with tools like Airflow, Kafka or Spark; containerise and monitor models Generative AI & LLMs - fine‑tuning, prompt engineering and evaluation of transformer‑based models (nice‑to‑have but strongly valued) with tools like Sagemaker or Bedrock Data visualisation & storytelling - build dashboards (e.g., Looker, Metabase) and craft narratives that drive decisions Cloud engineering - hands‑on with at least one major provider (AWS, GCP, or Azure) and infrastructure‑as‑code practices Bonus points for experience with real‑time event streams, reinforcement-learning-based personalisation, or privacy‑preserving ML

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

INCRMNTAL is searching for the best Data Scientist out there! We are a new startup founded by successful serial entrepreneurs with the vision to evolve digital marketing from the measurement of traffic to measuring VALUE. Advertising for the past 100+ years has faced a strange dilemma: "Half the budget I spend on Advertising is wasted. The problem is – I don't know which half" (John Wanamaker, 1838-1922) We are a deep machine learning and AI company, using the latest technologies, to solve a really simple question – "which half is wasted?" We are a new startup, launched in summer of 2020, backed by strategic investors from around the world who believe in what we're doing. We have customers and a constant flow of interest from the press, investors and more large companies who want to join us. Role Description: As a Staff Data Scientist your role will be to: Use analytical, statistical and technical skills to understand large, complex datasets with the aim to eliminate waste and measure incrementality for advertisers. Lead, Develop & improve algorithms for automated decision making around lifetime value, causality, multi-touch attribution, and media mix optimization Determine, analyze, maintain, and report on core marketing metrics to identify causal relationships between marketing actions and outcomes. Translate data into actions and recommendations – appropriately interpreting and building on findings, and fully exploiting insights You will also participate in important decisions around the product, our strategy as well as have the opportunity to choose the areas you work on based on what you want or like to do the most. We believe in helping each other get most of ourselves rather than do what we're not interested in doing. Key Requirements: Min. 8 years of work experience in a commercial data science environment that requires the combined application of statistical & engineering skills Strong statistical and research skills with a track record of using a variety of math and statistical methods (especially Causal Inference, Prediction models, Bayesian Method) Excellent development skills (Python required, R or Scala a plus) Experience in working on big data with distributed computing (Spark, Airflow, Kubernetes) Familiarity with digital marketing, advertising or analytics is a plus Extensive experience in the Time Series domain, working with small and large time series frames Theoretical background knowledge like from a relevant BSc, MSc or PHD is a big advantage (e.g. in Mathematics, Computer Science, Statistics, Physics) Please note: The requirements and description above represents the skills and expectations from the person in this role. Having said that - if you are really interested in the position and believe you only tick some of the key requirements - do apply. Benefits of Joining INCRMNTAL: We are a new startup with a LOT of supporters and positive attention from investors, press and customers. Joining early means that you could be part of our success. Excellent and committed employees will be granted share options. We are passionate, solution-oriented people who are accountable to our actions. We place our customers first and aspire to build a valuable solution to measuring value. At INCRMNTAL, you should be ready to make decisions yourself. We say: "Don't ask for permission – beg for forgiveness". We care and respect one another, as we are ALL people – our titles define our roles and responsibilities, they do not differentiate us. We are accepting applications regardless of any external factors. Everyone is welcome to apply. Send your CV and motivation letter (optional) to [email protected]

Pecan is an automated, AI-based predictive analytics platform that simplifies and accelerates the process of building and deploying machine learning models across various business use cases. Learn more about our dedicated offering here: demandforecast.ai. Pecan connects to the raw data and completely automates the data preparation, engineering, and preprocessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel's 50 most promising startups for two consecutive years. Company Highlights: Series C company with over $117M raised to date. Tier-1 investors: Google Ventures (GV), Insight Partners, GGV, Dell Ventures, Mindset, and S Capital. +50 employees HQ in Tel Aviv with a growing sales and marketing organization in the US Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson, Mars, and SciPlay. Demand Forecasting Team The Demand Forecasting team is at a pivotal, high-growth stage. We have successfully built our core pipeline and are now aggressively onboarding new, diverse customers. This role is highly dynamic, requiring a blend of technical depth, proactive research, and exceptional communication to adapt our platform to real-world customer data and challenges. You will be a critical link between the core product and our clients' business success. Responsibilities As a key Customer-Facing Data Scientist on the Demand Forecasting team, you will: Work at the forefront of the data world, focusing on the optimization and deployment of Demand Forecasting models. Handle complex data science challenges using state-of-the-art techniques relevant to demand forecasting and time series, such as forecasting model ensembling, anomaly detection, feature engineering for trend/seasonality, and causal inference modeling. Act as a highly communicative partner to our clients, diving deep into their unique datasets to understand the nuances and challenges of their business. Lead the rigorous testing and application of our core ML pipeline on new customer data, identifying areas where it performs exceptionally well and researching/developing solutions for complex cases where it does not. Be instrumental in helping our clients achieve their business goals and realize the measurable value of Pecan's Demand Forecasting module. Work within a production and product-oriented environment, ensuring models are robust, scalable, and directly integrated into the client's operational workflow. What We Are Looking For 5+ years of experience as a data scientist working on tabular and time-series data BSc in a relevant field (e.g., Computer Science, Engineering, Statistics) Proficient in Python, SQL, and Spark Proven experience in creating measurable value with ML (defining KPIs, designing A/B tests, monitoring models in production) Deep experience with time series modeling and forecasting techniques Experience with AWS / Databricks – an advantage MSc/Research experience – an advantage Experience working with Customers (in English) – an advantage

Papaya is a leading skill-based mobile gaming company, bringing together fun, competition, and real rewards for millions of players worldwide. Driven by our vision to create one of the world's most exciting player communities, we develop games powered by a large-scale B2C platform that supports tens of millions of daily tournaments and connects players around the world through social competitions. Located in the heart of Tel Aviv, we offer a fast-moving environment, culture of innovation, professional growth, and the opportunity to make a true impact. We run real-money skill-based games where many product decisions are prediction problems: who will churn, who will deposit again, what a fair match looks like, and whether a marketing cohort will pay back. We're hiring a Growth Data Scientist to replace heuristics with production models that directly influence what players see and do. This is not a research role you'll own problems end to end, from problem definition and labeling to data, modeling, backend integration, and measuring impact. Responsibilities Cohort LTV & Churn Prediction. A per-player risk score for depositors, labelled against repeat-deposit KPIs wired to real interventions, and cohort-level LTV:CAC maturation modelling to tell marketing in-month whether a cohort will pay back within a year. CRM & Lifecycle Optimization. Building models to trigger personalized Push and Email notifications at the exact right moment, matching the right communication journeys to the player's specific lifecycle stage and behavior. Deposit-after-N-days-without-deposit probability. A conditional/hazard model for the likelihood a lapsed depositor deposits again given N days of inactivity feeding RV model targeting and treatment timing. Lobby suggestion. Which tournaments and which entry-fee tiers to surface to which player. Today the lobby is driven by hand-built segmentation rules; we want a learned ranking model that trades off engagement, monetization, and matchmaking liquidity. Store suggestion. Personalized offer and pack ranking in the store, replacing static store-segmentation configs picking the right offer, at the right price point, at the right moment. Requirements 4+ years of applied data science in growth, monetization, retention or lifecycle with models you personally got into production and can point to a business decision they changed. Prior domain experience is required, not a bonus: mobile gaming, real-money gaming/iGaming, or a consumer app with a live in-app economy. You should already think in LTV, ARPDAU, D-n retention, offer elasticity, entry fees and payback windows without being taught the vocabulary. Hands-on propensity, churn, uplift, survival/hazard and LTV modelling. Recommendation or ranking experience is a strong plus. Python at production quality (LightGBM/XGBoost; PyTorch or TensorFlow for embedding-based work) and strong warehouse SQL (Snowflake, BigQuery). Real experimentation depth designed tests, sized them, and read them honestly. Including at least one case where a clean A/B wasn't available and you still produced a defensible impact claim.

We are looking for an outstanding and passionate Data Scientist with expertise in Machine Learning and AI to join our team. You will have the opportunity to research, design, implement, and evaluate a variety of advanced ML algorithms and AI models, while working with huge-scale, real-time data and taking projects end-to-end, from the business problem definition up to production. Responsibilities Apply cutting-edge solutions while leveraging our vast amounts of data, utilizing Machine Learning and AI techniques. Implement model usages from research to production, in a scalable and resilient architecture (and be part of creating and maintaining this architecture). Take projects end-to-end, from business questions and data exploration all the way to production. Collaborate with Data Scientists, Big Data Engineers, Product Managers, and Developers from across the organization to solve complex problems. Contribute to continuous learning and knowledge sharing within the Data Science group, to stay ahead of the curve in Machine Learning and AI advancements. Lead innovation initiatives and stay in tune with the latest technologies and algorithms. Requirements 3+ years of experience in Data Science / Machine Learning, or equivalent hands-on experience. Strong SQL knowledge. Solid programming skills - Python. Solid understanding of Machine Learning, statistics, and probability. Experience solving complex business problems using state-of-the-art Machine Learning and AI algorithms. Experience building and deploying ML models in production environment. Experience working with large-scale data - big data. Understanding of data pipelines, APIs, and cloud infrastructure. Good understanding of probability and statistics. Familiarity with ML frameworks and platforms such as Databricks, SageMaker, or Vertex AI. Ability to independently solve complex problems and make architectural decisions. Ability to mentor others, work in teams, and teach peers, and learn from them. Ability to handle several tasks simultaneously. Independent, out-of-the-box thinker. Advantage: Experience in AdTech, particularly programmatic advertising, bidding, targeting, or user-level modeling. Advantage: Experience with real-time ML systems, deep learning, LLMs, or reinforcement learning.

Overview Come build community, explore your passions and do your best work at Microsoft with thousands of University interns from every corner of the world. This opportunity will allow you to bring your aspirations, talent, potential—and excitement for the journey ahead.   As a Data Scientist Intern at Microsoft, you will combine applied AI, engineering, and scientific experimentation to build intelligent systems. Your work will focus on developing autonomous AI agents, evaluating large language models (LLMs), and automating research workflows. You will design experiments and apply statistical methods to assess and improve AI systems, accounting for the variability and uncertainty inherent in their non-deterministic behavior. Working with researchers, engineers, product teams, and other stakeholders, you will translate customer and business needs into practical solutions and measurable outcomes. You will combine analytical rigor with hands-on development to build prototypes and evaluation frameworks that improve the quality, reliability, and usefulness of AI-powered experiences. At Microsoft, Interns work on real-world projects in collaboration with teams across the world, while having fun along the way. You’ll be empowered to build community, explore your passions and achieve your goals. This is your chance to bring your solutions and ideas to life while working on cutting-edge technology. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities • Develop and improve autonomous AI agents and research workflow automations using LLMs, relevant algorithms, and appropriate data sources. • Explore emerging AI techniques, propose new ideas, and build proofs of concept (POCs) to assess their feasibility, potential value, and limitations. • Design experiments and evaluation frameworks for LLMs and AI agents, applying statistical methods to measure quality and reliability while accounting for non-deterministic behavior. • Explore data, analyze experimental results, and investigate failure modes to identify opportunities, validate approaches, and iteratively improve AI systems. • Communicate findings, limitations, and technical trade-offs clearly, providing actionable insights that inform product and engineering decisions. • Collaborate with researchers, engineers, product teams, and other stakeholders to translate customer and business needs into opportunities to enhance products and customer experiences through applied AI. **This student position is structured as a one-and-a-half-year role, concluding upon completion of the academic degree. Qualifications • Currently pursuing a Master’s degree in Data Science related fields:, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field. • At least 3 additional semesters before graduation – graduation date Winter 2028 or later. • knowledge of or experience working with Large Language Models (LLMs), including areas such as fine-tuning, modeling, evaluation, frontier models, or specialized models. • Hands-on experience writing code in a production environment and working with code repositories (e.g., Git). Preferred Qualifications: • Currently pursuing a PhD in Data Science related fields: Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Overview The WSSI team is looking for a motivated Data Science Intern to contribute to production-oriented AI solutions across computer vision and efficient small language models, helping bring advanced models to Windows products under real-world edge-device constraints.The team’s primary focus is developing state-of-the-art computer vision solutions that can run efficiently under constraints such as model size, latency, power, and deployment efficiency. In this internship, you will work with applied scientists, engineers, MLOps specialists, and data experts to help turn applied research and prototypes into high-quality AI experiences that can create impact for millions of customers. Responsibilities • Contribute to research, training, experimentation, evaluation, and prototyping of AI models in computer vision, efficient small language models, or related applied AI areas. • Support the development of production-oriented AI solutions, with attention to model quality, reliability, scalability, and measurable customer impact. • Analyze model behavior and performance under real-world edge-device constraints, including latency, footprint, power, and resource usage. • Contribute to exploring model optimization techniques including compression and efficient inference to enable deployment in resource constrained environments. • Work with datasets, evaluation pipelines, and experiment tracking to generate clear insights and support data-driven decisions. • Collaborate closely with product, system engineering, software, and silicon teams, and clearly communicate findings, tradeoffs, and recommendations. Qualifications Required Qualifications: • Currently pursuing an M.Sc. or Ph.D. in Computer Science, Electrical Engineering, Data Science, or a related technical field. • Hands-on experience with model development, evaluation, experimentation, and data analysis. • Practical Python development experience, including the use of modern AI-assisted coding tools. Preferred Qualifications: • Experience with computer vision systems, image/video processing, or AI models designed for real-world deployment. • Experience with model optimization, compression, quantization, distillation, efficient inference, or deployment on edge devices. • Experience working with data pipelines, model lifecycle workflows, MLOps concepts, or production AI infrastructure. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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.

The Data Scientist will be part of R&D Cell Therapy Media team, responsible for planning and execution of projects in co-operation with the rest of the team. The holder of this position will be responsible for the completion of product development projects with data science elements, integrating data and ML-based methods in cell culture technologies and for the accuracy of the results within the project timelines. The Data Scientist will be part of R&D Cell Therapy Media team, responsible for planning and execution of projects in co-operation with the rest of the team. The holder of this position will be responsible for the completion of product development projects with data science elements, integrating data and ML-based methods in cell culture technologies and for the accuracy of the results within the project timelines. Main Responsibilities & Tasks: • Methodology Development: Develop new data analysis methods and technologies in line with the department roadmap. This includes performing statistical design and evaluation of experiments, multivariate data analysis, and harnessing predictive modeling approaches. • ML Pipeline Management: Work on creating Machine Learning (ML) pipelines for the technology area, researching and implementing the right methodologies for different applications. • Continuous Improvement: Monitor model performance and make iterative improvements based on feedback and new data. • Innovation: Explore novel AI/ML approaches to enhance cell culture media development processes. • Project Leadership: Propose, execute and lead ML- and data-based work packages within defined projects in related technology areas according to company strategy and guidelines. This includes serving as a team member in cross-functional PMO projects. • IT & Data Infrastructure: Plan and drive implementation of required IT infrastructure for delivering project results. Support data collection from lab devices and other lab-related sources, ensuring seamless integration into the Sartorius data platform. • Reporting: Evaluate, analyze and present project results in internal and external meetings. • Expert Engagement: Actively participate in internal and external expert groups and/or networks in his/her technology area to bring in innovation and remain competitive. • IP Monitoring: Responsible for staying informed on and evaluating the IP situation for state-of-the-art technologies. • Data Pipeline Maintenance: Design, build, test and maintain robust data processing pipelines. Collaborate closely with other data science stakeholders to design and maintain data models for analysis and reporting. • Stakeholder Collaboration: Engage actively with local lab scientists as well as the Media Analytics team to better understand lab data, facilitating effective pipeline creation and optimization. Collaborate closely with the Data Science and Innovation team on data preprocessing and preparation. • Team Culture: Exemplify and foster a culture of continuous learning and innovation within the team. Qualification & Skills: • Education • Master’s degree in Science, Engineering or Computer/Data Science; minimum Bachelor's degree in a relevant field. • Basic understanding of SQL and database concepts. • Work Experience • Experience in industry with a proven track record (experience in respective technology areas of Sartorius is a plus). • Methodological Competence • Very good written and verbal English communication skills. • Ability to work self-driven and with a high level of self-motivation. • Strong analytical and conceptual thinking skills to solve complex problems. • Willingness to learn new data analytical technologies and curiosity about new fields like cell biology. • Interest in cloud data platforms and data engineering. • Confident handling of MS Office. • • 3+ years of work experience (or 7+ years of work experience with a Bachelor's degree). • Solid working knowledge of Python is required, with familiarity in libraries such as Pandas, scikit-learn and other Python ML libraries. • Experienced in statistical analysis and relevant programming languages and software tools. • Deployment capabilities and adaptation of existing data-driven solutions to new applications. About Sartorius Sartorius is part of the solution in the fight against cancer, dementia, and many other diseases. Our technologies help translate scientific discoveries into real-world medicine faster, so that new therapeutics can reach patients worldwide. We look for ambitious team players and creative minds, who want to contribute to this goal and advance their careers in a dynamic global environment. Join our global team and become part of the solution. We are looking forward to receiving your application. www.sartorius.com/careers

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.

Honeycomb Insurance At Honeycomb, we're not just building technology; we’re reshaping the future of insurance. In 2025, Honeycomb was recognized by Dun & Bradstreet as “Top 10 Best Start Up Companies to Work For” in Israel, named by LinkedIn as “Top 10 Startups in Chicago”, and Newsweek’s "Greatest Startup Workplaces in America, 2025". Through the first half of 2026 we’ve been recognized on Inc. Magazine’s "Best Workplaces" List and Forbes "Fintech 50". How did we earn these honors? Honeycomb is a rapidly growing global startup, generously backed by top-tier investors and powered by an exceptional team of thinkers, builders, and problem-solvers. Dual-headquartered in Chicago and Tel Aviv (R&D center), and with 6 offices across the U.S., we are reinventing the commercial real estate insurance industry, an industry long overdue for disruption. Just as importantly, we ensure every employee feels deeply connected to our mission and one another. With over $100B in insured assets, Honeycomb operates across 23 states, covering more than 65% of the U.S. population and increasing its coverage. If you’re looking for a place where innovation is celebrated, culture actually means something, and smart people challenge you to be better every day - Honeycomb might be exactly what you’ve been looking for. • About The Role We are looking for a Senior Data Science Researcher to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics. In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method often has to be invented for the problem. What You'll Do - Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal. - Work across the team's core research areas — risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes into reliable predictive signal. - Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations grounded in that understanding. - Design approaches from first principles when off-the-shelf methods don't fit the data. - Build rigorous evaluations and own the result in production, not just the notebook. - Document research clearly so findings are reproducible, auditable, and compound over time. Requirements - MSc. or PhD in Statistics, Applied Math, Physics, or a closely related quantitative field. - Real research experience - a track record of driving original investigations end-to-end (academic research, a research-heavy PhD, industry R&D, or equivalent), not only applied ML delivery. - Strong mathematical or statistical problem-solving instincts - able to model a messy real-world system from scratch, not just apply a library. - Production deployment experience: monitoring, CI/CD, data validation, reproducibility. - Ability to independently initiate, plan, and drive entire research directions. - Team player, positive, driven, independent, fast learner. Advantages - Depth in classical statistics, causal inference, or applied probability. - Deep learning experience. - Clean coding and repository-maintenance habits.