- Master’s degree in Statistics or Economics, or a quantitative discipline, or equivalent practical experience.
- Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages.
- PhD degree in a quantitative discipline
- 2 years of experience in working as a Data Scientist, including expertise with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data
- Experience with machine learning on large datasets
- Understand potential outcomes framework and have familiarity with causal inference methods (e.g., split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators). Knowledge of structural econometric methods.
About the job
At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google’s business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google’s practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.
As a Data Scientist, you will evaluate and improve Google’s products. You’ll collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, using statistical methods for the challenges of measuring quality, improving consumer products, and understanding the behavior of end-users, advertisers, and publishers.
- Help suggest, support, and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product, and customer facing teams.
- Collaborate with teams to define relevant questions about advertising effectiveness, incrementality assessment, the impact of privacy, user behavior, brand building, targeting, and bidding, and develop and implement quantitative methods to answer those questions.
- Find ways to combine large-scale experimentation, statistical-econometric, machine learning, and social-science methods to answer business questions at scale.
- Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution, and answer strategic questions using data.
- Work with large, complex data sets. Conduct end-to-end analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and written and verbal delivery of results to business partners and executives.