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.

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.

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

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]

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.

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.

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

About Pagaya Pagaya is a global technology company making life-changing financial products and services available to more people nationwide, as it reshapes the financial services ecosystem. By using machine learning, a vast data network and a sophisticated AI-driven approach, Pagaya provides comprehensive consumer credit and residential real estate solutions for its partners, their customers, and investors. Its proprietary API and capital solutions integrate into its network of partners to deliver seamless user experiences and greater access to the mainstream economy. Pagaya has offices in New York and Tel Aviv. For more information, visit pagaya.com. About the Role We are looking for a Lead Data Scientist to lead strategic research initiatives. This individual will be at the forefront of Pagaya’s research, working hands-on to advance our models while also mentoring and collaborating with data scientists across the department. Responsibilities • Spearhead strategic research of new cutting edge Machine Learning models • Mentor and advise data scientists in the Auto loans research team as well as across the research department • Design solutions for the company’s core mission using Data Science methodologies from various domains • Work in a short life-cycle research and development environment • Generate and test working hypotheses in unexplored territory Requirements • 8+ years of experience in Machine Learning, working with tabular data on predictive modeling • Track record of translating research into business impact • MSc in a related field- Math/EE/CS/Physics/Stats • Great interpersonal skills, and strong collaborator • Good applied statistics skills, such as distributions, statistical testing, regression, etc. • Proficient in Python and the Data Science Stack • Independent and fast learner that can work alone and in teams • Knowledge in some of the following can be an advantage: optimization, risk and finance Our Team Pagaya was founded in 2016 by seasoned research, finance, and technology entrepreneurs, and we are now 500+ strong in New York, Los Angeles, and Tel Aviv. We move fast and smart, identifying new opportunities and building end-to-end solutions from AI models and unique data sources. Every Pagaya team member is solving new and exciting challenges every day in a culture based on partnership, collaboration, and community. Join a team of builders who are working every day to enable better outcomes for our partners and their customers. Our Values Our values are at the heart of everything we do. We believe great solutions are built through a great community. • Advance Inclusion- We create a world where everyone can win, designing systems that better represent people and generate sustainable value for our employees, partners and investors. • Be Accountable Together- We proudly own our actions and our results, taking initiative to ensure our work gets over the finish line as a team. • Continuously Learn- We challenge ourselves for the sake of getting better as individuals, as teams, and as an organization to deliver for our partners. • Debate and Commit- We respectfully and openly debate to strengthen our ideas and build shared conviction - once we decide, we go all in, together. • Dream Big and Act- We boldly tackle complex problems, pressure-test solutions in real-time, and adapt with speed and energy. More than just a job We believe health, happiness, and productivity go hand-in-hand. That's why we're continually looking to enhance the ways we support you with benefits programs and perks that allow every Pagayan to do the best work of their life. Pagaya is an equal opportunity employer. Pagaya is encouraging diversity and actively seeking applicants from all backgrounds, as are committed to creating a diverse workforce together with an inclusive environment for all. Employment is decided on the basis of qualifications, skills, and business needs. https://pagaya.com/privacy-policy

About Pagaya Pagaya is a global technology company making life-changing financial products and services available to more people nationwide, as it reshapes the financial services ecosystem. By using machine learning, a vast data network and a sophisticated AI-driven approach, Pagaya provides comprehensive consumer credit and residential real estate solutions for its partners, their customers, and investors. Its proprietary API and capital solutions integrate into its network of partners to deliver seamless user experiences and greater access to the mainstream economy. Pagaya has offices in New York and Tel Aviv. For more information, visit pagaya.com. About the Role The Group Lead is a critical leadership position within Pagaya. This role involves managing a high-performing group responsible for driving profitability and strategic decision-making through advanced data analytics, optimization strategies, and predictive modeling related to loan funding and issuance (specifically in the Personal Loans product, which is Pagaya’s largest product). This is primarily a management-focused role, requiring a high level of technical understanding to guide the team, combined with the professional maturity and executive presence necessary to effectively collaborate with and present to company leadership. Responsibilities • Leading, mentoring and managing a multidisciplinary group comprising Data Scientists, Data Analysts and Optimization Specialists. • Defining and executing overall strategy, ensuring alignment with the company’s financial goals and product roadmap. • Oversighting of the group's projects, initiating new ideas and proactively improving the company’s abilities. • Fostering a culture of data-driven decision-making, continuous learning, and high-velocity iteration within the team. • Directing the team in performing sophisticated data analytics to identify trends, market anomalies, and segmentation opportunities impacting profitability. • Leading the development and implementation of optimization algorithms and strategies aimed at maximizing loan portfolio performance, minimizing risk, and increasing overall profitability. • Translating complex data insights into clear, actionable business recommendations for partnerships and operational teams, to ensure seamless integration of data-driven solutions. • Serving as the primary representative of the group, presenting complex findings, strategic recommendations, and group performance metrics to the company's executive team and other departments. • Guiding the development, validation, and deployment of advanced predictive models to enhance performance. Requirements • 8+ years professional experience in Data Science or Data Analytics, preferably within FinTech. • Proven experience 5+ years managing and mentoring multidisciplinary teams of Data Scientists and Analysts in a fast-paced environment. • High-level understanding of statistical modeling, machine learning techniques, and optimization algorithms. • Master's degree or Ph.D. in a quantitative field such as Data Science, Statistics, Mathematics, Operations Research, or Computer Science. • Descent knowledge in programming languages (e.g., Python) and databases (SQL). Experience with cloud computing environments is a plus. • An authoritative leader who navigates high-stakes environments with strategic maturity and exceptional communication. Our Team Pagaya was founded in 2016 by seasoned research, finance, and technology entrepreneurs, and we are now 500+ strong in New York, Los Angeles, and Tel Aviv. We move fast and smart, identifying new opportunities and building end-to-end solutions from AI models and unique data sources. Every Pagaya team member is solving new and exciting challenges every day in a culture based on partnership, collaboration, and community. Join a team of builders who are working every day to enable better outcomes for our partners and their customers. Our Values Our values are at the heart of everything we do. We believe great solutions are built through a great community. • Advance Inclusion- We create a world where everyone can win, designing systems that better represent people and generate sustainable value for our employees, partners and investors. • Be Accountable Together- We proudly own our actions and our results, taking initiative to ensure our work gets over the finish line as a team. • Continuously Learn- We challenge ourselves for the sake of getting better as individuals, as teams, and as an organization to deliver for our partners. • Debate and Commit- We respectfully and openly debate to strengthen our ideas and build shared conviction - once we decide, we go all in, together. • Dream Big and Act- We boldly tackle complex problems, pressure-test solutions in real-time, and adapt with speed and energy. More than just a job We believe health, happiness, and productivity go hand-in-hand. That's why we're continually looking to enhance the ways we support you with benefits programs and perks that allow every Pagayan to do the best work of their life. Pagaya is an equal opportunity employer. Pagaya is encouraging diversity and actively seeking applicants from all backgrounds, as are committed to creating a diverse workforce together with an inclusive environment for all. Employment is decided on the basis of qualifications, skills, and business needs. https://pagaya.com/privacy-policy

About Pagaya Pagaya is a global technology company making life-changing financial products and services available to more people nationwide, as it reshapes the financial services ecosystem. By using machine learning, a vast data network and a sophisticated AI-driven approach, Pagaya provides comprehensive consumer credit and residential real estate solutions for its partners, their customers, and investors. Its proprietary API and capital solutions integrate into its network of partners to deliver seamless user experiences and greater access to the mainstream economy. Pagaya has offices in New York and Tel Aviv. For more information, visit pagaya.com. About the Role We are looking for a Senior Data Scientist to lead strategic research initiatives . This individual will be at the forefront of Pagaya’s research, working hands-on to advance our models while also mentoring and collaborating with data scientists across the department. Responsibilities • Spearhead strategic research of new cutting edge Machine Learning models • Mentor and advise data scientists. • Design solutions for the company’s core mission using Data Science methodologies from various domains • Work in a short life-cycle research and development environment • Generate and test working hypotheses in unexplored territory Requirements • 5+ years of experience in Machine Learning, working with tabular data on predictive modeling • Track record of translating research into business impact • MSc or PhD in a related field- Math/EE/CS/Physics/Stats • Great interpersonal skills, and strong collaborator • Good applied statistics skills, such as distributions, statistical testing, regression, etc. • Proficient in Python and the Data Science Stack • Independent and fast learner that can work alone and in teams • Ability to learn and explore new fields of expertise • Knowledge in some of the following can be an advantage: optimization, risk and finance Our Team Pagaya was founded in 2016 by seasoned research, finance, and technology entrepreneurs, and we are now 500+ strong in New York, Los Angeles, and Tel Aviv. We move fast and smart, identifying new opportunities and building end-to-end solutions from AI models and unique data sources. Every Pagaya team member is solving new and exciting challenges every day in a culture based on partnership, collaboration, and community. Join a team of builders who are working every day to enable better outcomes for our partners and their customers. Our Values Our values are at the heart of everything we do. We believe great solutions are built through a great community. • Advance Inclusion- We create a world where everyone can win, designing systems that better represent people and generate sustainable value for our employees, partners and investors. • Be Accountable Together- We proudly own our actions and our results, taking initiative to ensure our work gets over the finish line as a team. • Continuously Learn- We challenge ourselves for the sake of getting better as individuals, as teams, and as an organization to deliver for our partners. • Debate and Commit- We respectfully and openly debate to strengthen our ideas and build shared conviction - once we decide, we go all in, together. • Dream Big and Act- We boldly tackle complex problems, pressure-test solutions in real-time, and adapt with speed and energy. More than just a job We believe health, happiness, and productivity go hand-in-hand. That's why we're continually looking to enhance the ways we support you with benefits programs and perks that allow every Pagayan to do the best work of their life. Pagaya is an equal opportunity employer. Pagaya is encouraging diversity and actively seeking applicants from all backgrounds, as are committed to creating a diverse workforce together with an inclusive environment for all. Employment is decided on the basis of qualifications, skills, and business needs. https://pagaya.com/privacy-policy

Description Lumenis is a global leader in energy-based solutions for Aesthetics and Eye Care. Headquartered in Yokneam, Israel, with offices in Tel Aviv, Lumenis has a strong global presence and business performance worldwide. We are looking for a Data Insights & Analytics Manager who will develop a deep understanding of data generated by Lumenis' global installed base of connected medical devices, combine it with enterprise data, and translate it into insights and tools that create value for our customers and the business. The role goes beyond reporting and dashboard creation. We are looking for someone who is naturally curious, proactively explores data, identifies patterns and opportunities, and drives the organization to act on what the data reveals. Importantly, the role is expected to uncover broader customer, commercial, and business insights by connecting IoT data with enterprise and other relevant data sources - not only insights derived from device data itself. Main Duties: This is a highly hands-on analytics role at the intersection of data, technology and business, requiring both strong analytical capabilities and a product mindset. Data Foundation & BI • Develop a deep understanding of Lumenis’ IoT data ecosystem, including device events, data structures, business logic, and KPIs. • Work hands-on with data across AWS and enterprise systems. • Partner with BI to define requirements, maintain trusted metrics and models, and improve data quality and usability. • Independently build, maintain, and enhance dashboards and analytics solutions in Amazon QuickSight. Analytics & Insights • You will be expected not only to answer business questions with data, but to identify the questions we should be asking. • Proactively explore complex IoT and business datasets to uncover meaningful trends, patterns, anomalies, and opportunities. • Translate complex data into clear, actionable insights and proactively bring relevant findings to the business. Business & Commercial Analytics • Partner with commercial teams to improve customer engagement, strengthen sales narratives, and develop data-driven tools. • Create compelling, evidence-based data stories for internal and external audiences. • Measure the impact of data-driven initiatives and refine them based on results. Data Activation & Adoption • Drive adoption of analytics tools and data-driven practices across the organization. • Train business users to interpret and apply data effectively. • Proactively communicate relevant insights rather than relying solely on self-service dashboards. • Continuously identify new ways data can create value for customers and the business. Key Interfaces • BI; Sales and Commercial Excellence; Marketing; Service; Product; Finance and Operations; and regional business teams. Requirements • Significant experience in BI, business analytics, data analytics, or a similar role. • Strong SQL skills and hands-on experience with BI platforms such as QuickSight, Power BI, or Qlik. • Ability to independently explore complex datasets and understand data structures, business logic, and KPI definitions. • Strong analytical curiosity, business acumen, and a track record of translating insights into commercial opportunities and actions. • Excellent data visualization, storytelling, communication, and stakeholder-management skills. • A proactive, independent approach, with the ability to drive adoption and influence without formal authority. • Additional advantages include experience with: • IoT, connected-device, or product-usage data. • AWS, S3, Salesforce, SAP, or Python. • Medical devices, healthcare, SaaS, or another connected installed-base business. • Customer-engagement or commercial analytics in a global organization.

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.