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As an Applied Scientist on the incrementality foundations team within marketing measurement, you will contribute to Uber Marketing Org’s commitment to science-backed decision making. This team is responsible for building experiment forms, models, & processes for turning complex hypotheses into reliable, actionable incrementality signals that inform planning, forecasting, and investment decisions across the business. Your work will focus on building robust processes for interpreting measurement data, building foundational models for estimation & inference, and researching experimentation best practices for accurate and complete incrementality measurement. Your work will inform smarter investment and decision-making systems across Uber's Brand and Performance Marketing. This team sits close to production systems, partnering with Product, Engineering, and cross-functional Science teams to ensure incrementality measurement is rigorous, scalable, and decision-ready
Job Responsibility:
Develop and apply statistical and causal inference models to estimate the incremental impact of marketing across channels, markets, and test designs
Design custom tests & analyze results from complex experiments, including multi-cell, market-level, and longitudinal tests
Contribute to foundational modeling efforts such as hierarchical smoothing, aggregation across tests, and handling of low-signal or sparse data
Assist in research on advanced topics, including learning elasticity with experiments and analyzing event relationships to improve experiment accuracy and interpretation
Partner with Product and Engineering to integrate incrementality models into reporting and decision-making workflows
Research and help establish best practices for experiment design, post-analysis interpretation, and measurement tradeoffs
Collaborate with other Applied Science and Data Science teams to align incrementality outputs with broader measurement and investment frameworks
Requirements:
Bachelor’s degree or higher in a quantitative field such as Statistics, Economics, Mathematics, Computer Science, or a related discipline
2+ years of experience applying statistical and causal inference methods to real-world data in an applied setting
Strong foundation in statistical modeling, hypothesis testing, and experimental design, including A/B testing, incrementality, or quasi-experimental methods
Hands-on experience designing, analyzing, and interpreting experiments, including evaluation of uncertainty, limitations, and tradeoffs
Proficiency in Python or R and SQL for analyzing and modeling large, complex datasets
Experience working with marketing, growth, advertising, or similar business domains where measurement and investment decisions are central
Ability to clearly communicate analytical insights to technical and non-technical stakeholders and collaborate effectively in cross-functional teams
Demonstrated ability to collaborate effectively in cross-functional, fast-moving environments
Nice to have:
Bachelor's, Master’s or PhD in a quantitative discipline (e.g., Statistics, Economics, Mathematics, Computer Science), or equivalent applied industry experience
Experience with advanced statistical or modeling techniques such as hierarchical / multi-level models, time-series analysis, or meta-analysis
Experience with incrementality testing, geo-experiments, or marketing measurement use cases (e.g., attribution, MMM, or related frameworks)
Familiarity with marketing investment concepts such as elasticity, budget allocation, or cross-channel tradeoffs
Experience contributing to shared codebases or production-adjacent analytics systems, including collaboration with Product or Engineering partners
Ability to independently drive scoped projects end-to-end with guidance, and operate effectively in ambiguous or evolving problem spaces
Curiosity and learning mindset, with demonstrated ability to ramp quickly on new domains, tools, or methodologies
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan