Product Data Scientist, engEDU

Google•San Jose, CA

2d•$138,000 - $198,000

About The Position

Engineering Education (engEDU) is Google’s engineering education organization, dedicated to upskilling Googlers and equipping them to be highly productive builders. AI is reshaping developer roles faster than our training keeps pace. Systems thinking, judgement, agent architecture and management are becoming core skills for every builder. To keep with this pace, we are moving more towards an in-product, integrated technical training model to help Googlers become AI-native builders. Our team is expanding our engineering footprint to develop agentic systems and personalized coaching agents directly into the tools engineers already use, like Jetski (Antigravity), Cider, Critique, and Buganizer. We don’t want to saturate these interfaces with noisy nudges common in traditional in-product learning. Rather, we want to engage engineers exactly when it is most relevant to avoid disrupting their work. Shaping the real dimensions and interpreting what the data actually tells us is the massive opportunity we have for a Data Scientist. We are blazing a trail, and would love to have you at the forefront of designing education for the era of AI and at the scale of Google.

Requirements

Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or 2 years of experience with a Master's degree.

Nice To Haves

Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).

Responsibilities

Design telemetry and data dimensions that will help pinpoint where developers hit roadblocks and evaluate how they use various interventions over time.
Pair insights from quantitative and qualitative data to identify friction points for engineers, validate working hypotheses, measure what true mastery looks like for new in-flow experiences, and uncover new opportunities for engineering education.
Translate data insights into strategic product decisions, directly shaping engineering education at Google's scale in the era of AI.