Minimum qualifications:
- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 7 years of experience with statistical data analysis, modeling, experimentation, causal inference, data mining, querying, and managing analytical projects with programming in SQL, Python, or R.
- 7 years of experience developing and managing metrics or evaluating programs/products.
- 5 years of experience as a people manager within a technical leadership role.
- Master's degree or PhD in machine learning, statistics, a related field, or equivalent practical experience.
- Experience with the full ML product lifecycle, from ideation to exploration, productionization, and long term support.
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About the job
Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.
In this role, you will partner with teams across the company to apply Google's best Data Science techniques to Google's enterprise opportunities. The team partners with Research, Core Enterprise ML, and ML Infrastructure teams to build solutions for enterprise.
Responsibilities
- Lead and inspire a team of data scientists, provide technical guidance and career development support, and delegate and conduct performance reviews.
- Define the data science idea and roadmap, prioritize initiatives with measurable business impact, and stay current with industry trends and emerging technologies.
- Oversee data science projects, ensuring timely delivery and quality, define project scope, objectives, and success metrics, and manage project risks and resource allocation.
- Maintain knowledge in statistical modeling, machine learning, and data mining, contribute to the development of advanced analytics solutions, and evaluate and implement new tools and technologies.
- Build cross-functional relationships, communicate technical concepts clearly, present data-driven insights to senior management, and collaborate with engineering, product, and business teams.