Applied Scientist Ii, Safety

Uber Uber · Consumer · San Francisco, CA · Data Science

This role focuses on applying machine learning to enhance safety within Uber's platform. The responsibilities include owning the end-to-end applied science workflow, from problem scoping and deep-dive analysis to developing models and driving experimentation. The candidate will work cross-functionally with product, engineering, and operations to deliver impactful safety solutions and will be responsible for deploying sophisticated ML models into production, ensuring their robustness and measurable safety impact. Experience with LLMs in a production environment is preferred.

What you'd actually do

  1. Design, develop, and deliver sophisticated applied ML models from ideation to production, ensuring robustness and measurable safety impact.
  2. Conduct complex, rigorous deep-dive analyses and causal inference to uncover root causes and identify high-leverage safety opportunities.
  3. Own the design, analysis, and interpretation of A/B experiments to rigorously evaluate product and policy changes before platform rollout.
  4. Partner closely with Product Managers, Engineers, and Policy teams to translate data-driven insights into critical product features and company-wide safety policies.

Skills

Required

  • core machine learning principles
  • classification
  • regression
  • time series analysis
  • causal inference
  • Python
  • Scala
  • SQL

Nice to have

  • Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, Economics
  • safety
  • risk
  • fraud
  • LLM
  • high scale production implementations

What the JD emphasized

  • building and deploying machine learning models or conducting high-impact applied data science in a large-scale production environment
  • Hands-on experience with LLM including high scale production implementations

Other signals

  • end-to-end applied science workflow
  • deliver impact
  • deliver sophisticated applied ML models from ideation to production
  • measurable safety impact
  • rigorous deep-dive analyses and causal inference
  • high-leverage safety opportunities
  • rigorously evaluate product and policy changes
  • partner closely with Product Managers, Engineers, and Policy teams
  • translate data-driven insights into critical product features and company-wide safety policies
  • building and deploying machine learning models or conducting high-impact applied data science in a large-scale production environment
  • Hands-on experience with LLM including high scale production implementations