Data Science Team Leader

About the role

Ready for a challenge? That's good, because at Just Eat Takeaway.com (JET) we have abundant opportunity, or, as we say, everything is on the table. We are a leading global online food delivery marketplace. Our tech ecosystem connects millions of active customers with hundreds of thousands of connected partners in countries across the globe. Our mission? To empower every food moment around the world, whether it's through customer service, coding or couriers. About this role As the Senior Team Lead for Data Science - Courier Pay & Incentives, you will be the strategic leader responsible for maximising the impact of our real-time courier incentive systems on supply quality, earnings fairness, and operational efficiency across our global delivery network. This is a high-impact, real-time-first leadership role. Courier pay and incentive systems operate under strict latency and fairness constraints - decisions made in milliseconds affect earnings for hundreds of thousands of couriers and directly shape supply availability at the moment demand arrives. Your team sits at the intersection of economics, real-time ML, and logistics operations. You will own the data science strategy for courier compensation models, dynamic boost and surge mechanisms, engagement incentives, and the behavioural analytics that underpin fair and effective pay design. Experience in real-time systems, experimentation at scale, and economic modelling is essential for providing credible technical leadership to a senior team of Data Scientists. Location: Hybrid- 3 days a week from our London, Berlin or Amsterdam office & 2 days working from home Reporting to: Head of Data & Analytics Location: Berlin, London or Amsterdam office with 3 days in the office and 2 days working from home Reporting to: Data Science Manager These are some of the key components to the position: Team Leadership: Mentor and manage a high-performing Data Science team, fostering a culture of speed, rigor, and courier-centric problem-solving. Strategy & Roadmap: Own the data science strategy for real-time courier pay systems, including dynamic earnings, surge pricing, and incentive programs. KPI Ownership: Drive critical business outcomes by taking ownership of key courier supply metrics like availability, acceptance rates, and earnings competitiveness. Root Cause Analysis & Communication: Diagnose structural and behavioral supply issues and translate complex economic models into actionable narratives for cross-functional stakeholders. Real-Time Architecture: Design the conceptual framework for real-time incentive engines, balancing advanced model sophistication with strict latency and reliability constraints. Dynamic Pricing: Oversee the development of predictive models that address local supply-demand imbalances and optimize real-time boost levels. Fairness & Technical Standards: Act as the senior technical guide to ensure pay models are rigorous, auditable, mathematically consistent, and free from unintended bias. MLE Collaboration: Partner closely with Machine Learning Engineers to ensure smooth, robust deployment and scaling of production-ready models. Causal Experimentation: Lead the design and execution of complex marketplace experiments to accurately measure the real-world impact of pay and incentive changes. Behavioral Modeling & ROI: Direct the modeling of courier behavior (elasticity, churn, engagement) and build frameworks to maximize the ROI of incentive spend using large-scale geospatial data. What will you bring to the team? Proven, extensive experience in leadership and people management, with a demonstrated ability to mentor, guide, and develop Data Scientists. Prior hands-on experience developing, deploying, and maintaining machine learning models in a corporate environment. This experience is crucial for providing effective strategic and architectural guidance. Advanced conceptual proficiency in data science and machine learning methodologies, ideally with experience in logistics, geospatial analysis, and ETA prediction or routing problems. Experience with deep learning is considered a plus. Demonstrated experience in root-cause analysis of complex production model performance issues and the ability to translate those findings into effective business and technical solutions. Strong understanding of the model lifecycle and best practices, including testing, code reviews, and monitoring. Exceptional communication and stakeholder management skills, with the ability to influence technical peers and non-technical business leaders. At JET, this is how we play Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. Being the best at what we do isn't just about delivering on our strategy. It's a competition for something incredibly valuable - our customers' choice. Every time a customer decides where to order, they're picking a side. At the heart of the JE

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