Data Scientist, Product, Search AI Experiences

Google LLC; Mountain View (WY); On-site; USD 138,000 - 197,000; Full time

Job summary

Google LLC is seeking a data science professional to shape AI-powered search metrics and evaluation methods. You will design experiments for complex user journeys, analyze results, and translate insights into actionable product directions that influence AI features and search quality.

The role requires strong statistical analysis skills, proficiency in Python/R/SQL, and the ability to work across Engineering, Product, UX, and Research teams.

Qualifications

• Bachelor's degree in a quantitative field or equivalent experience with 5 years of analysis-applications experience and coding (Python, R, SQL), or 2 years with a Master's degree.
• Experience with extracting insights, statistical analysis, or solving business problems using data science methods.
• Ability to frame problems, communicate results to both technical and non-technical audiences.

Responsibilities

• Define and shape success metrics and evaluation approaches for AI-powered search experiences.
• Design experiments for multi-step user flows to understand impact of AI features.
• Translate findings into product direction and influence prioritization with data.
• Collaborate cross-functionally with Product, Engineering, UX, and Research teams.

Skills

Statistical analysis

Python

R

SQL

Experiment design

Education

Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field

Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field

Job description

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on.
In this role, you will work cross-functionally with leads from Engineering, Product, UX, and Research to shape how AI-powered experiences in Search are measured and improved. As AI systems across the industry become more capable at reasoning through complex, multi-step goals, the question of what "success" means for a search journey is evolving, and you will help define that answer: by developing new metrics and evaluation approaches, designing experiments for complex user flows, and translating findings into product direction. Your work will directly inform the future of Google Search and AI Mode, applying techniques like LLM-based evaluation and causal inference to some of the most consequential product questions at Google.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

Minimum qualifications:

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

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL).
• Frame and sol ve ambiguous product problems and communicate the results clearly to both technical and non-technical audiences.
• Work cross-functionally with Product, Engineering, UX, and Research partners. Act as a thought partner and influence prioritization with data.
• Define success metrics and quality frameworks for multi-step and conversational AI experiences in Search, spanning task completion, efficiency, trust, and user satisfaction.
• Analyze human and au to-rater evaluations of AI experiences alongside behavioral signals to build a holistic view of product quality.
• Design and assess live experiments to understand the impact of new AI features on user journeys, and make clear launch recommendations.