Responsibilities

• Direct the design and development of innovative measurement methodologies and products, setting the strategic technical direction for Conversion Lift and incrementality measurement.
• Architect and oversee the execution of complex experimental frameworks, including both user-level and geo-based randomized controlled trials, to rigorously establish causal impact at scale.
• Advance quantitative methods by integrating causal inference, statistical modeling, and machine learning techniques to solve highly ambiguous and complex measurement challenges.
• Drive the creation of scalable analysis pipelines, setting technical standards for the team and mentoring other data scientists on end-to-end analysis best practices.
• Serve a key technical lead and trusted cross-functional partner, and a mentor to junior data scientists.

Requirements

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.

Preferred

• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Experience with causal inference methods such as split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators.
• Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
• Applied experience with machine learning on datasets.