Google – Data Scientist, Local Growth

August 13, 2023
Google – Data Scientist, Local Growth

Job Description

Minimum qualifications:
Master’s degree in Statistics or Economics, or a quantitative discipline, or equivalent practical experience.
3 years of experience in statistical data analysis.
Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages

Preferred qualifications:
Master’s degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or equivalent practical experience
5 years of experience in a data analysis field
Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages (e.g. SQL)
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.

Work cross-functionally with multiple teams (e.g., UX, Software Engineering, Product Management, Analysis, etc.) across Local and Search to advance our metric capabilities.
Use advanced modeling (e.g., MUM or ranking based models) for Machine Learning (ML) based metrics, logs-data for metric tuning, human rater based data, and user-survey data.
Develop metric, product performance investigation, experiment design, and growth analysis.
Partner with the Local Search team to focus on user-facing experiences across multiple products.
Work cross-functionally to identify metric improvements.