Data Scientist, Research, Search Platforms

Google•Cambridge, MA

2h•$174,000 - $253,000

About The Position

Search Platform Data Science team works on understanding, measuring, and improving Search systems, including but not limited to Experiment, Reliability, Velocity, Latency, Capacity, Content, Regulation, and Antiscraper. Our team sits at the intersection of Advanced Causal Inference, Product Analytics, and Machine Learning. As a Data Scientist on this team, you will pioneer methodological innovations to capture and improve the end-to-end user experience. This includes scaling User-Perceived Reliability metrics, applying advanced causal inference techniques to measure the impact of user friction on long-term retention, and engineering creative workarounds—like using LLMs to synthesize ground-truth labels—when clean data is unavailable. Furthermore, you will drive analytical excellence across the organization by authoring frameworks and best practices to educate the broader Data Science community. 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: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.

Nice To Haves

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.
Ability to take open-ended problems, provide better definition and eventually solutions.

Responsibilities

Extract strategic insights from large and complex datasets spanning client-side and server-side logs. Address intricate, non-routine analytical challenges by utilizing advanced methodologies as required to optimize the user experience for AIM, AIO products.
Execute comprehensive, end-to-end analyses encompassing data ingestion, requirements specification, processing, modeling, continuous deliverables, and professional presentations. Build and prototype analysis pipelines iteratively to provide insights at scale.
Interact cross-functionally with a wide variety of people and teams. Work closely with engineers (e.g., Platform, Product, and etc.) to identify opportunities for, design, and assess improvements to AI products.
Make strategic business recommendations (e.g., estimating growth headroom, cost-benefit of engineering optimizations, and marketing campaign effectiveness) with effective presentations of findings to multiple levels of leadership.