Data Scientist, Research, Search Platforms

The Search Platform Data Science team at Google focuses on understanding, measuring, and improving Google Search systems, with an emphasis on the end-to-end user experience. In this role, you will work at the intersection of Advanced Causal Inference, Product Analytics, and Machine Learning, helping teams make measurement and decision-making stronger across complex, real-world environments.

You will be responsible for methodological innovation and analytics execution, from defining how to measure user experience to building scalable pipelines and translating results into recommendations. The work spans topics including Experimentation, Reliability, Velocity, Latency, Capacity, Content, Regulation, and Antiscraper.

Role focus

• Understand and improve Search systems by combining measurement approaches with analytical rigor across the user journey.
• Pioneer methodological innovations to capture and improve end-to-end user experience, including scaling User-Perceived Reliability metrics.
• Apply advanced causal inference techniques to measure the impact of user friction on long-term retention.
• Prototype and apply creative workarounds when clean data is unavailable, including using LLMs to synthesize ground-truth labels.
• Drive analytical excellence across the organization by authoring frameworks and best practices to educate the broader Data Science community.

Responsibilities

• Extract strategic insights from large, complex datasets that include client-side and server-side logs, tackling intricate, non-routine analytical problems to optimize the user experience for AIM and AIO products.
• Lead end-to-end analyses, including data ingestion, requirements specification, processing, modeling, ongoing deliverables, and professional presentations.
• Build and iteratively refine analysis pipelines to provide insights at scale.
• Collaborate cross-functionally with engineers and multiple teams (including Platform and Product) to identify opportunities, design improvements, and assess impact for AI products.
• Develop strategic business recommendations, such as growth headroom estimates, cost-benefit assessments of engineering optimizations, and marketing campaign effectiveness, and present findings to multiple leadership levels.

Requirements

• Master’s degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• Minimum experience: 5 years using analytics to solve product or business problems, including coding (Python, R, SQL) and querying databases or conducting statistical analysis; alternatively, 3 years with a PhD.
• Ability to take open-ended problems, define them more precisely over time, and work toward solutions.

Preferred qualifications

• 8 years using analytics to solve product or business problems, including coding (Python, R, SQL) and querying databases or conducting statistical analysis; alternatively, 6 years with a PhD.

Technologies

• Python
• R
• SQL
• LLMs

Location and compensation

• Location: Mountain View, CA (onsite)
• Salary: USD 174,000 - 253,000 per year
• Bonus target: 15%
• Additional compensation: equity
• Benefits: benefits

Work location preference

• Applicants can share a preferred working location from: Mountain View, CA, USA; Cambridge, MA, USA