Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
- Experience with Machine Learning (ML) product lifecycle from ideation to exploration, productionization, and long-term support.
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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 help develop data leveraged solutions that optimize the operational efficiency of our internal stakeholder teams. You will drive awareness with our internal customers throughout the corporate engineering organization about how to effectively manage and use data for strategic advantage, model critical business operations and deploy AI/ML solutions. You will provide quantitative support to strategic partners to the business by delivering data-driven insights and solutions. You will be using data to help them make better business decisions and weave stories with meaningful insight from data.
Responsibilities
- Lead research projects that explore novel applications of data science and machine learning within our business.
- Design, implement, and evaluate sophisticated statistical and machine learning models, pushing the boundaries of performance and efficiency in an enterprise environment.
- Work with Machine Learning (ML) Software Engineers to translate research findings into scalable and production ready solutions.
- Refine models and algorithms through experimentation and optimization and understand trade-offs and design choices.
- Stay up-to-date on the latest developments in data science and machine learning research internally and externally, identify and evaluate new techniques and technologies.