Google

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Sunnyvale, CA, USA.

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 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.
• 5 years of experience with modern machine learning techniques (such as sequence or transformer modeling experience in NLP, Vision, or speech domains).

Preferred qualifications:

• 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
• Experience delivering bespoke analytics to executive stakeholders (e.g., problem scoping/definition, modeling, interpretation, presentation).
• Experience with large datasets in a large scale distributed system.
• Experience with human eval data.

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.

Through your professional expertise and judgment, you work on multiple projects concurrently, developing analyses, models and methods to support and verify the impact of your recommendations. You share your expertise by consulting on projects, serving on committees and mentoring team members.

We're seeking a technical and Staff Data Scientist to be a key driver of innovation. In this role, you will use your skills in machine learning and a strong foundation in core data science methodologies. You will be instrumental in conceiving and deploying solutions, directly contributing to the advancements of AI. Your ability to translate tests into opportunities will be critical as you collaborate closely with executive stakeholders.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Work with large, complex datasets. Contribute to solving complex and ambitious data science problems.
• Present to executive stakeholders and executives, effectively defend your work, and influence strategy and product direction.
• Interact cross-functionally with a wide range of product and model teams. Work closely with Product Managers and engineers to identify opportunities.
• Define key metrics to measure success in various shapes and forms.
• Develop new methodologies to improve the performance of Google's models through better training data, including data acquisition, and insights.