Apply causal inference and differential privacy methods to design experiments, assess attribution, and develop privacy-preserving marketing products.
Execute end-to-end analyses including data gathering, EDA, and model development to deliver strategic insights to executives.
Build iterative analysis pipelines and prototype data structures to provide scalable insights across Google’s complex data ecosystems.
Collaborate with Product and Engineering team to define and answer quantitative questions regarding incrementality, user behavior, and bidding optimization.
Provide technical guidance and prioritization for the team, conduct cost-benefit analyses to drive high-level business decisions and product strategy.
Minimum qualifications:
Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
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 with statistical data analysis such as linear models, multivariate analysis, causal inference, or sampling methods.
Experience with statistical software (e.g., SQL, R, Python, MATLAB, pandas) and database languages along with statistical analysis, modeling and inference.
Preferred qualifications:
PhD in a quantitative field.
10 years of experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, and sampling methods.
5 years of leadership experience, including people management.
Experience with Machine Learning (ML) on large datasets, with the ability to select the right statistical tools in a given data analysis problem.
Understanding of potential outcomes framework and with causal inference methods such as split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators, with knowledge of structural econometric methods.
Ability to set and drive technical strategy.
