Staff Data Scientist, Ads Delivery

Pinterest Pinterest · Consumer · Palo Alto, CA · Monetization

Staff Data Scientist for Ads Delivery at Pinterest. This role involves developing a deep understanding of ads delivery, leading projects on Ads Delivery opportunities, designing and productionizing ML and evaluation frameworks for forecasting, recommendation, and causal inference, advocating for best-in-class experimentation, and collaborating across disciplines. The role requires expertise in ML, statistical modeling, causal inference, product analytics, and programming.

What you'd actually do

  1. Develop a deep, nuanced understanding of the Pinterest ads delivery, quantifying full funnel opportunities and risks.
  2. Lead projects on: Ads Delivery opportunities across different funnel stages
  3. Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
  4. Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
  5. Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.

Skills

Required

  • Machine Learning (recommendation, ranking, prediction, experimentation)
  • Statistical Modeling & Causal Inference (observational and experimental data)
  • Product analytics/strategy
  • Programming in Python/R
  • Advanced SQL/Spark
  • Scientific rigor
  • Exceptional communication
  • Cross-functional leadership

Nice to have

  • Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience

What the JD emphasized

  • 10+ years of hands-on experience in web-scale data environments
  • Deep expertise in: Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy
  • Track record mentoring and growing data talent at the staff/senior IC level.

Other signals

  • ML and evaluation frameworks
  • forecasting, recommendation, and causal inference
  • experimentation, instrumentation, and metric design