Data Science Team Leader

• 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

• 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
• 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

Experience with deep learning is considered a plus