Senior Applied Scientist

Uber Uber · Consumer · New York, NY +1 · Data Science

This role focuses on applying statistical and econometric methods to measure and optimize marketing budgets, build technical tools for marketing problems (like media mix models and optimization engines), and collaborate with cross-functional teams to provide strategic insights. It involves complex data analysis and guiding marketing strategy.

What you'd actually do

  1. Apply statistical/econometric methods to estimate the incremental impact of changes in marketing and product strategy on brand-relevant KPIs like sentiment, awareness, and purchase intent – as well as on business outcomes like trips/orders and gross bookings.
  2. Build and improve technical tools for marketing problems. Examples include media mix models, statistical estimators to gauge the impact of various interventions (experimental or otherwise), optimization engines that determine how to best distribute resources, etc.
  3. Interface closely with external partners (media agencies, advertising platforms, measurement vendors) to deeply understand the existing capabilities and limitations of their platforms and solutions, and to push the envelope on what is possible.
  4. Collaborate with our in-house media, research, product, and operations teams to develop actionable customer insights and recommendations to guide high level business and marketing strategy.

Skills

Required

  • statistical modeling
  • causal inference
  • econometric techniques
  • SQL
  • Python
  • R
  • communication skills
  • problem-solving

Nice to have

  • marketing/AdTech quantitative problems
  • PhD in statistics, economics, or operations research
  • intellectual curiosity
  • innovative technical solutions

What the JD emphasized

  • 5+ years of experience (or equivalent) with statistical modeling, causal inference, and econometric techniques.
  • Proficiency in SQL and a data analysis programming language – Python (preferred) or R
  • Excellent communication skills to understand stakeholder needs, set expectations around feasibility, translate business challenges into research questions, and present findings to technical and non-technical audiences
  • Be self-driven and show the ability to deliver on ambiguous projects with messy and incomplete data