Uber is looking to enhance the safety of its platform by proactively mitigating safety events and making rare safety events even rarer through the application of cutting-edge data science and machine learning.
Requirements
- Expert proficiency in core machine learning principles, including classification, regression, time series analysis, and causal inference.
- High proficiency in at least one programming language (e.g., Python or Scala) and expertise in data manipulation using SQL.
- Demonstrated experience designing and delivering end-to-end ML solutions that operate at significant scale (handling large datasets and high-velocity systems).
- Applied knowledge of core insurance concepts such as: Risk Modeling: Understanding of concepts like frequency, severity, and loss development.
- Loss Cost & Pricing: Familiarity with how safety events translate into financial loss (expected claims/payouts) and the inputs for risk-based pricing or economic valuation of safety interventions.
- Telematics: Experience leveraging granular sensor or telematics data to model driver behavior and assess accident probability.
Responsibilities
- Define the strategic roadmap and set the technical direction for developing and deploying large-scale, high-performance machine learning systems focused on proactive safety prediction and mitigation.
- Design, develop, and deliver sophisticated applied ML models from ideation to production, ensuring robustness and measurable safety impact.
- Conduct complex, rigorous deep-dive analyses and causal inference to uncover root causes and identify high-leverage safety opportunities.
- Own the design, analysis, and interpretation of A/B experiments to rigorously evaluate product and policy changes before platform rollout.
- Partner closely with Product Managers, Engineers, and Policy teams to translate data-driven insights into critical product features and company-wide safety policies.
Other
- Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field, OR equivalent experience.
- 8+ years (with Ph.D.) or 10+ years (with M.S. or B.S.) of industry experience building and deploying machine learning models or conducting high-impact applied data science in a large-scale production environment.
- Proven track record of influencing product or policy decisions using rigorous analysis, experimentation (A/B testing), and clear communication of complex technical results to non-technical stakeholders.
- For New York, NY-based roles: The base salary range for this role is USD$212,000 per year - USD$235,500 per year.
- For San Francisco, CA-based roles: The base salary range for this role is USD$212,000 per year - USD$235,500 per year.
