As a Data Scientist for Search Quality, you will drive measurement technology innovation at the core of the Search Quality. You will spearhead the development of an automated eval platform designed for next-generation experimentation. In this role, you will pioneer novel synthetic modeling techniques that translate multifaceted real-world behavioral and cognitive signals into high-fidelity simulations. You will build a cutting-edge Large Language Model (LLM)-based framework and predictive scoring engines to model complex user dynamics at scale. Furthermore, you will establish advanced validation and backtest simulation frameworks against large-scale historical benchmarks to dramatically accelerate the frontier of product quality evaluation. 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 joining the Search team, you'll have an opportunity to make an impact on billions of people globally. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities • Pioneer novel generative modeling and simulation techniques, translating multimodal data and behavioral signals into high-fidelity synthetic evaluation frameworks. • Architect and iterate on next-generation LLM-based pipelines, building scalable predictive engines to model complex cognitive dynamics, sentiment, and latent user behaviors. • Design and execute rigorous validation paradigms, establishing backtesting frameworks against historical benchmarks to verify simulation fidelity and predictive precision. • Drive cross-functional innovation with engineering, research, and product teams to integrate automated evaluation platforms into the core experimentation lifecycle. • Conduct advanced analytical and statistical investigations on large-scale discovery environments, developing new methodologies to accelerate product quality measurement and algorithmic iteration. Minimum qualifications: • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field. • 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree. Preferred qualifications: • 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree. • Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. Experience translating analysis results into business recommendations. • Applied experience with machine learning on large datasets. • Ability to select the right statistical tools given a data analysis problem along with effective communication skills. • Excellent leadership and self-direction skills along with willingness to both teach others and learn new techniques.