AddressMinimum qualifications:
- Bachelor's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases, or statistical analysis.
Preferred qualifications:
- 4 years of experience working as a Data Scientist with statistical data analysis such as linear models, multi-variate analysis, stochastic models, sampling methods.
- Experience articulating and translating business questions and using techniques to arrive at an answer using available data.
- Experience with causal inference methods (e.g., split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators).
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.
Responsibilities
- Help, suggest, support, and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product, and customer facing teams.
- Work with datasets, solve analysis problems, apply analytical methods, conduct analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and delivery of results to business partners and executives.
- Build and prototype analysis pipelines to provide insights at scale, develop comprehensive knowledge of Google data structures and metrics, and advocate for changes where needed for product development.
- Interact cross-functionally, making business recommendations (e.g., cost-benefit analysis, experimental design, use of privacy preserving methods such as differential privacy), with presentations of findings at multiple levels of stakeholders to drive business decisions and stakeholder thinking.
