Director, Analytics, Commerce Platform

DoorDash DoorDash · Consumer · San Francisco, CA · 331 Analytics

Director of Data Science to lead a broad and high-impact portfolio supporting Commerce Platform. This role will shape how success is measured, guide product and commercial strategy, and build data foundations for faster, better decisions across products at different stages of maturity. The goal is to build a high-performing team, improve decision-making quality and speed, and identify/scale high-ROI opportunities.

What you'd actually do

  1. Lead and grow a team of data science and analytics leaders and practitioners across a portfolio that spans 0→1 products, products still being shaped and commercialized, and businesses already scaling quickly.
  2. Own the analytical direction for the portfolio, including experimentation, KPI design, forecasting, decision support, funnel analytics, and foundational measurement.
  3. Partner closely with Product, Engineering, Strategy & Operations, and Go-to-Market leaders to influence roadmap, commercialization, and investment decisions.
  4. Build the operating system for decision-making by improving instrumentation, creating single sources of truth, and connecting short-term execution with long-term business outcomes.
  5. Operate with broad scope and high autonomy in a startup-like environment while benefiting from the support and talent density of a scaled analytics organization.

Skills

Required

  • 10+ years of experience in Data Science, Analytics, or a related quantitative field
  • significant experience leading senior teams and developing managers
  • strong product and business analytics judgment
  • track record of defining metrics, building decision frameworks, and influencing strategy in ambiguous environments
  • deep expertise in experimentation and causal inference
  • comfortable making decisions in settings with imperfect data, evolving products, and small or noisy samples
  • hands-on technical fluency in SQL and statistical analysis
  • experience working cross-functionally with Product, Engineering, and business leaders
  • translate complex analysis into clear recommendations that drive action

What the JD emphasized

  • lead senior teams
  • developing managers
  • ambiguous environments
  • imperfect data
  • evolving products
  • small or noisy samples
  • technical fluency in SQL
  • statistical analysis