Business Data Scientist Intern, PhD, Summer 2027

Location: Mountain View, CA, USA; Ann Arbor, MI, USA; Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Bellevue, WA, USA; Cambridge, MA, USA; Chicago, IL, USA; Irvine, CA, USA; Kirkland, WA, USA; Los Angeles, CA, USA; Madison, WI, USA; New York, NY, USA; Palo Alto, CA, USA; Portland, OR, USA; Pittsburgh, PA, USA; Raleigh, NC, USA; Durham, NC, USA; Reston, VA, USA; Redmond, WA, USA; Redwood City, CA, USA; San Diego, CA, USA; Goleta, CA, USA; San Bruno, CA, USA; Seattle, WA, USA; San Francisco, CA, USA; San José, CA, USA; Santa Cruz, CA, USA; South San Francisco, CA, USA; Sunnyvale, CA, USA; Washington D.C., DC, USA

As a Business Data Scientist, you will be a subject matter specialist for translating business problems from your supported organization or functional area into analytical solutions and insights. You will provide technical mentorship in delivering project work including implementing data science solutions, improving data pipelines, developing evaluation metrics, or building statistical models that provide insights to the business. You will create and implement reused and scaled solutions within the team's development process. You will collaborate with supported teams and win stakeholder trust by translating business needs into tractable analyses or evaluation metrics. You will work closely with product and engineering teams to analyze impact on product or Google-wide metrics (including daily activities, retention, churn).

Google is and always will be an engineering company. We hire people with a broad set of technical skills who are ready to address some of technology's greatest challenges and make an impact on millions, if not billions, of users. At Google, engineers not only revolutionize search, they routinely work on massive scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, Social to Local, Google engineers are changing the world one technological achievement after another.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $118000 - $157000 (USD) + 0% bonus target Learn more about benefits at Google .

Work with large complex data sets, solve difficult non-routine analysis problems, and apply advanced analytical methods. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.

Build and prototype analysis pipelines iteratively to provide insights at scale. Develop a comprehensive understanding of Google data structures and metrics, advocating for changes.

Interact cross-functionally, making business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.

Develop and automate reports, and iteratively build and prototype dashboards to provide insights at scale, solving for business priorities.

Design and analyze controlled experiments or counterfactual causal inference studies to examine the incremental impact of business, product, or go-to-market initiatives.

Minimum qualifications

Currently pursuing a PhD degree in a quantitative discipline (e.g., statistics, biostatistics, physics, applied mathematics, operations research, economics).

Experience with statistical methods (i.e., linear models, multivariate analysis, stochastic processes, sampling methods, etc.).

Experience with statistical software (e.g., R, Python, MATLAB) and database languages (i.e., SQL).

Preferred qualifications

Currently attending a degree program in the US and available to work full time for 12 weeks outside of university term time.

In their penultimate academic year or returning to a degree program after completion of the internship.

Experience leveraging data insights into storytelling for business stakeholders.

Experience in controlled experiment design and causal inference methods.

Ability to prioritize requests and partner well in an environment with competing demands from stakeholders.