Research Data Scientist, Payments
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Minimum qualifications:
- Master's degree in Statistics or Economics, or a quantitative discipline, or equivalent practical experience.
- 8 years of experience as a statistician, bioinformatician, or data scientist, with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- 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 data analysis or related fields (e.g., statistician, data scientist, computational biologist, bioinformatician).
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 make Google Pay a trusted payments brand for users and merchants globally. You will help deliver a Payments Platform that makes it easy and fast to build excellent and consistent payment experiences across Google products.
Responsibilities
- Bring the analytic, statistical aptitude, and data science principles to drive strategic and tactical product and business decisions.
- Engage with cross-functional teams for different product initiatives across consumers, merchants and payments platform, and help define, implement, and evaluate measurement methodology to drive decisions.
- Combine large-scale experimentation, statistical-econometric, machine learning, and social-science methods to answer business questions at scale.
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