• Build and own data infrastructure — pipelines, warehousing, ETL/ELT, data quality; make sure the foundation is solid • Analyze product usage and user behavior — identify patterns, segment users, surface what matters from the noise; think like a product person, not just a data person • Build models that ship — LLM-based systems, traditional ML (classification, clustering, NLP), evaluation frameworks; whatever the problem needs • Define and track the metrics that matter — activation, retention, engagement, PQLs; connect data to product and GTM decisions • Run experiments and measure impact — A/B tests, causal analysis, cohort studies; rigorous but fast • Turn data into product conviction — you don’t just hand off charts, you tell the team what to do and why

• You'll work directly on the AI systems powering Binance AI Products and next-generation agentic trading features — alongside the full-time algorithm team, on real production challenges. • You own deliverables, run experiments, and ship code that matters. You build components of AI systems (agents, pipelines, evaluation tools), debug real systems, and work with engineers to ship features that reach users. • This is not a "watch and learn" program. You are expected to build, contribute, and ship. • Contribute to the design and development of LLM-powered pipelines for agentic trading — including reasoning agent components, tool-use frameworks via MCP, and automated workflow execution across crypto markets. • Build and evaluate prompt engineering strategies, test-time scaling approaches, and retrieval architectures for crypto-native data sources — on-chain data, market feeds, news, and sentiment signals. • Design and run model evaluation experiments — defining quality metrics for agent reasoning in financial contexts, executing benchmarks, and synthesizing results into actionable findings. • Analyze agent performance characteristics in live trading scenarios — covering decision accuracy, latency, reliability, and adversarial robustness. • Apply AI-native development practices using agentic coding tools as a standard part of the engineering workflow — writing and executing Python code for strategy logic, data pipelines, and agent evaluation.

About Zego At Zego, we understand that traditional motor insurance holds good drivers back. It's too complicated, too expensive, and it doesn't reflect how well you actually drive. Since 2016, we have been on a mission to change that by offering the lowest-priced insurance for good drivers. From van drivers and gig workers to everyday car drivers, our customers are the driving force behind everything we do. We've sold tens of millions of policies and raised over $200 million in funding. And we’re only just getting started. About the Team We don't build pricing models — we build the driving intelligence that feeds them. Our mission is fairer insurance, priced on how people drive, not on who they are, and safer roads. The Telematics team turns raw phone sensor data into meaningful signals about how people drive. Using signal processing and machine learning on high-frequency GPS, accelerometer, and gyroscope data, we extract the behavioural features that power Zego's understanding of driving quality, context, and risk. The Telematics Data Science team is based in Portugal (Porto and Lisbon) and collaborates daily with engineering, product, and actuarial colleagues across the UK and PT. We're one team across Portugal and the UK. PT and UK roles carry the same scope, ownership, and progression. Decisions get made where the work happens, not in a single headquarters. This isn't a solo remote seat; you'll join an established team of peers. About the Role Few teams use phone sensor data to price commercial motor insurance at scale. As a Senior Data Scientist, you'll shape how millions of trips are turned into risk signals. You'll own behavioural features and algorithms from hypothesis to production, working at the intersection of data science and engineering on GPS, inertial, and other sensor data. You'll move between quick heuristics and full ML models depending on what the problem calls for. You ship code to production, not just notebooks. What You'll Be Doing - Work with raw, high-frequency sensor data: GPS, accelerometer, gyroscope, at scale. This isn't warehouse-tabular data: it's noisy, physical, and where the signal actually lives. You'll be processing more than 250k trips per day. - Research new behavioural features and detection algorithms: read the literature, try ideas, kill the ones that don't survive contact with real data. - Design and build behavioural features and factors that feed Zego's understanding of driving quality, context, and risk. - Take ideas from hypothesis to production: prototyping in notebooks, then writing the production-grade Python and SQL that scales. - Prioritise simple, robust solutions, rule-based when that's enough, ML when it's warranted. - Lead experiments, validate impact with data, and automate insight generation. - Collaborate closely with software engineers, product managers, and and actuaries to get features into the pricing path and measure their effect on real policies. - Translate complex sensor data into clear findings that non-technical stakeholders can act on. About You - MSc (or integrated MEng/MSc) in a quantitative field: Engineering, Computer Science, Physics, Mathematics, or similar. This is a core role that requires strong engineering foundations. - Working knowledge of digital signal processing or sensor physics — you can reason about noise, sampling, filtering, and the physical meaning behind a signal, not just its numbers. - Proven track record delivering data science or data engineering projects into production. You write production-grade code, not just notebooks. - Strong Python and SQL (we use Snowflake). Comfortable with the scientific Python stack: Polars, Pandas, NumPy, SciPy, and scikit-learn.  - Experience developing and evaluating ML models on tabular data — classification or regression tasks where evaluation matters as much as model choice. Think passenger-vs-driver detection, transport-mode classification, or score predictiveness. - You've designed, built, and maintained data pipelines from scratch: reliable, observable, and scalable. - Fluent with AI coding assistants as part of daily engineering work. We use Claude Code across the team and expect candidates to be comfortable working with tools of this kind. - Strong communicator: you translate ambiguous problems into structured, testable ideas, and share insights clearly with technical and non-technical audiences. - Growth mindset: curious, open to feedback, driven to keep improving. Nice to Have - Experience applying signal processing to noisy, high-frequency sensor/time-series data (GPS, accelerometer, IMU) in production. - Exposure to insurance, mobile data, or behaviour modelling. - Experience with cloud platforms (AWS), containerisation (Docker, Kubernetes), or data orchestration frameworks. - Familiarity with our ML and tooling stack: MLflow for experiment tracking, DVC for data versioning, Streamlit for internal apps and demos. What’s it like to work at Zego? Joining Zego is a career-defining move. People go further here, reaching their full potential to achieve extraordinary things.  We’re spread throughout the UK and Europe, and united by our drive to get things done. We’re proud of our company and our culture – a friendly and inclusive space where we can lift each other up and celebrate our wins every day. Together, we’re setting the bar higher, delivering exceptional work that makes a difference. Our people are the most important part of our story, and everyone here plays a role. There’s loads of room to learn and grow, and you’ll get the freedom to steer your career wherever you want. You’ll work alongside a talented group who embrace each other's differences and aren’t afraid of a challenge. We recognise our achievements, learn from our mistakes, and help each other to be the best we can be. Together, we’re making insurance matter.  How we work We believe that teams work better when they have time to collaborate and space to get things done. We call it Zego Hybrid. While some of our team choose to come into our central London office once a week, we’re flexible - some people prefer being in once a month or even quarterly. It’s all about finding the right balance between collaborative face time and focused home-working, so we can achieve great results while maintaining a healthy work-life balance. Our approach to AI We believe in the power of AI to meaningfully improve how we work - helping us move faster, think differently, and focus on what matters most. At Zego, we encourage people to stay curious and intentional about how AI is leveraged in their work and teams to drive practical impact every day. This is your chance to do the most meaningful work of your career - and we’ll provide you with the tools, support, and freedom to do it well. Benefits We reward our people well. Join us and you’ll get a market-competitive salary, private medical insurance, company share options, generous holiday allowance, and a whole lot of wellbeing benefits. We also offer an annual flexible hybrid working contribution, which you can use to support with your travel to the office or towards your own personal development. And that’s just for starters!  There’s more to Zego than just a job - Check out our blog for insights, stories, and more. We’re an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, or disability status. #LI-IL1 #LI-Hybrid

• Lead end-to-end design of high-complexity models, including deep learning, advanced forecasting, optimization, and NLP. • Define data science strategy and roadmaps in partnership with senior leadership. • Architect feature engineering frameworks, experimentation standards, and modeling conventions. • Oversee deployment and integration of models using Spark and Databricks. • Monitor model performance and detect model drift in production environments. • Mentor L1 and L2 Data Scientists, standardize best practices for code and modeling.