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.

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