• Translate complex business challenges into well-defined technical problems solvable with data, statistics, and machine learning.
• Collaborate with product managers, engineers, and business stakeholders to build and scale data science solutions that power driver allocations, surge pricing and driver incentives.
• Own the full ML lifecycle—from ideation and research to model development, pipeline implementation, deployment, experimentation, and driving measurable business outcomes.
• Improve the efficiency of our dynamic driver incentives using techniques from machine learning, causal inference, optimization and simulation
• Design and interpret experiments to measure model impact, working with analysts and product teams to ensure rigorous evaluation and clear success metrics.
• Monitor and evaluate model performance, identifying areas for improvement and proposing solutions
• Communicate insights, trade-offs, and technical decisions effectively to cross-functional stakeholders, and operate with a high degree of autonomy in ambiguous problem spaces.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Machine Learning, or a related quantitative field
• Solid understanding of statistics and machine learning fundamentals, with coursework or projects demonstrating practical application.
• Proficiency in Python and SQL, and familiarity with data analysis or modeling libraries.
• Strong analytical thinking and problem-solving skills, with the ability to reason from data and communicate findings clearly.
• A willingness to learn fast, take initiative, and work collaboratively in a cross-functional team.
• Curiosity, humility, and a drive to apply data science to real-world problems at scale.