Minimum qualifications:
- Master's degree in Statistics, Economics, a related quantitative field, or equivalent practical experience.
- 10 years of experience in statistical modeling, including team management.
- Experience with statistical software (e.g., R, Python, MATLAB, Pandas) and database languages.
- PhD degree in a quantitative discipline.
- 6 years of experience working as a Data Scientist, including 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.
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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.
In this role, you will create and plan campaigns to serve the right ads to the right users and help allocate resources to the right channels and allocate bids and budgets across campaigns appropriately. You will audit the efficacy of ads in delivering on various campaign goals. You will ensure users are exposed to ads they find useful and for publishers, content creators and other ad-technology firms can generate business appropriately.
Responsibilities
- 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, bidding. Develop and implement quantitative methods to answer those questions.
- 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 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.