Staff Data Scientist: Measurement Innovation Leader

Google; New York (NY); Hybrid; USD 207,000 - 300,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Google in New York, NY, USA is seeking a Staff Data Scientist focused on Measurement Methodology Innovation. You will lead analytics to innovate measurement tech for search quality, building automated evaluation platforms and large-scale simulations.

Strong quantitative skills and leadership are required for cross-functional impact. Responsibilities include designing automated evaluation pipelines, validating models with historical benchmarks, and translating insights into actionable business

Qualifications

• Master's degree in statistics, data science, mathematics, physics, economics, or a related quantitative field.
• 8+ years of analytics experience or 6+ years with a PhD, applying analytics to product or business problems.

Responsibilities

• Pioneer novel generative modeling and simulation techniques translating multimodal data into synthetic evaluation frameworks.
• Architect and iterate on next-generation LLM-based pipelines to model cognitive dynamics and user behavior.
• Design and execute rigorous validation paradigms with backtesting against historical benchmarks.
• Drive cross-functional innovation with engineering, research, and product teams to integrate automated evaluation platforms.
• Conduct advanced analytics investigations on large-scale discovery environments to accelerate product quality metrics.

Skills

Analytics leadership

Python

SQL

ML on large data

Statistical modeling

Education

Master's degree in a quantitative field

Job description

Google in New York, NY, USA is seeking a Staff Data Scientist focused on Measurement Methodology Innovation. You will lead analytics to innovate measurement tech for search quality, building automated evaluation platforms and large-scale simulations.

Strong quantitative skills and leadership are required for cross-functional impact. Responsibilities include designing automated evaluation pipelines, validating models with historical benchmarks, and translating insights into actionable business