Senior Research Data Scientist, Merchant Shopping Data Science

The Merchant Data Science team is a group of data scientists (US, London, Zurich) within the Merchant Shopping Organization. We work on building scalable data products that empower data-driven decision-making.

As a Senior Data Scientist, you are eager to innovate data science using genAI tools and build durable, impactful data products. You are a full-stack expert who can bridge the gap between software engineering, data engineering, and data science. You will evaluate and improve Google's Shopping products. You will collaborate with a multidisciplinary team of engineers and Product Managers on a wide range of problems. You will bring problem-solving excellence and statistical methods to the challenges of measuring quality, improving shopping products, and understanding the behavior of merchants, end-users, and partners. You will solve non-routine problems, build data products, inform strategic decisions, and drive impact on cross-functional projects.

The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Identify the underlying need, process complex datasets, and apply advanced analytical methods when needed.
  
• Provide data-driven perspectives on product direction and strategy, analyzing data and defining metrics to identify opportunities and risks.
  
• Design, build, and scale innovative data products, including self-serve tools, automated pipelines, and experimentation frameworks.
  
• Advance model, product quality, and foundational understanding through LLM-based evaluations, leveraging human evaluations, auto-raters, and advanced logs-based analysis.
  
• Partner cross-functionally with engineers, product managers, and leadership, delivering clear business recommendations based on data.
  

Minimum qualifications:

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

Preferred qualifications:

• 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.
  
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Applied experience with machine learning on large datasets.
• Demonstrated skills in selecting the right methodology given a data analysis problem, and effective written and verbal communication skills.
• Demonstrated leadership and self-direction, with willingness to both teach others and learn new techniques.