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.

About the job

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.

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.