Drimva

Google

Business Data Scientist Intern, PhD, Summer 2027

Mountain View, CA, USA +30 · Internship · Posted Sep 14, 2026

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
Education
Currently pursuing a PhD degree in a quantitative discipline.

About the job

Translates business and product questions into scalable data science analyses, models, experiments, dashboards, and recommendations for Google teams.

What you'll do

  • Analyze complex data and build scalable analysis pipelines, reports, and dashboards.
  • Design and analyze controlled experiments or causal inference studies to measure initiative impact.
  • Present findings and business recommendations to stakeholders and collaborate with product and engineering teams.

What they ask for

  • Currently pursuing a PhD in a quantitative discipline.
  • Experience with statistical methods.
  • Experience with statistical software such as R, Python, or MATLAB and database languages such as SQL.

Nice to have

  • Research published in ML/AI venues, particularly on LLMs, generative AI, or model evaluation; hands-on deep learning or applied ML/LLM experience.
  • Experience with unstructured text, AI coding tools and agentic workflows, forecasting, econometrics, and frequentist or Bayesian analysis.

Pay

USD 118000 - 157000 base salary

Tools

R, Python, MATLAB, SQL, Cursor, Codex, Claude Code

AI focus

Preferred work includes ML/AI research and applied machine learning, including LLMs, generative AI, model evaluation, deep learning, and agent development.

Model-written summary · the original posting is authoritative

First seen Oct 21, 2026 · Last verified Nov 5, 2026 (UTC)