About this role
As the Senior Team Lead for Data Science – Logistics Estimations, you will be the strategic leader responsible for maximizing the impact of our predictive engine on the global delivery network. Your primary focus will be providing vision, strategic oversight, and leadership to a fully experienced team of Data Scientists specializing in Estimated Time of Arrival (ETA) prediction and other critical logistics estimations.
Collaborating on the roadmap, driving business outcomes, and expertly managing and developing your team.
Experience in model development and deployment is essential for providing effective technical guidance and strategy to the team.
Your core mission is to elevate the accuracy of pre-purchase and post-purchase estimated delivery time models by translating business performance challenges into data science solutions.
Location: Berlin, London or Amsterdam office with 3 days in the office and 2 days working from home.
Reporting to: Data Science Manager.
Responsibilities
- 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.
Qualifications
- 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.
- 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 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.
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