• PhD in a quantitative field (IE Statistics, Mathematics, Physics)
• 3+ years of industry experience solving large-scale complex problems
• Proven expertise using advanced statistical methods (e.g., ML models, hypothesis testing, causal analysis)
• Ability to interpret and work with data in the presence of ambiguity (e.g., confounding factors, data sparsity or quality challenges)
• Demonstrated knowledge of Python/SQL/R data analysis libraries and packages
• Ability to take ambiguous and complex business requirements and propose a technical solution

Desirable

• Examples of areas the data science team is involved in include safety readiness evaluation, development of signals to characterize good driving, evaluation of ML models in production systems, and simulation technology (virtual driving)
• In this hybrid role, you will report to the Manager of Data Science
• Develop evaluation frameworks for both autonomous vehicle performance and the evaluation frameworks themselves ("eval of eval")
• Develop statistical methods and ML models to analyze historical/simulation data
• Frame and solve ambiguous problems and communicate the results
• Develop new metrics, interpret trends, and investigate anomalies in data from simulation and our rider-only service and share findings with leadership
• Be an advisor and influence collaborators across multiple teams
• Mentor other data scientists, either formally (as a tech lead) or less formally (as an individual contributor)