Job Description
About the Role
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
What the Candidate Will Need / Bonus Points
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.
Basic Qualifications
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.
Preferred Qualifications
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.
For New York, NY-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.
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
What the Candidate Will Need / Bonus Points
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.
Basic Qualifications
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.
Preferred Qualifications
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.
For New York, NY-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.
💡 Quick Summary
Seeking a career-building opportunity? The Applied Scientist, Marketing Measurement position is now open for candidates interested in the Work from home Jobs sector. This role in New York offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Work from home Jobs is a plus.
