Business Data Scientist, Applied Machine Learning, GCS

• Design, develop, and validate robust causal inference models (e.g., Synthetic Control, Difference-in-Differences, Double Machine Learning) to isolate the incremental impact of GCS programs.
• Partner with business teams to design and execute A/B tests, defining the sample sizes, power analyses, and success metrics required for valid results.
• Track the latest academic research in Causal ML and Econometrics, proactively prototyping new methods to improve the precision of impact estimates.
• Translate highly technical methodologies into clear, prescriptive business narratives for non-technical executive audiences.
• Establish comprehensive monitoring systems to track model performance, detect data drift, and ensure the ongoing accuracy of deployed measurement frameworks.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
  

Preferred qualifications:

• PhD in a quantitative discipline such as Computer Science, Engineering, Economics, Statistics, Mathematics, Physics, Neuroscience, or equivalent practical experience.
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
  
• Experience in driving a project from an experimental idea to a proof-of-concept to a launched product feature.
• Experience in publications and working with technologies.

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.