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Job Description
Business Data Scientist, Applied Machine Learning, GCS corporate_fare Google place Mountain View, CA, USA ; New York, NY, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. info_outline
X Note:
By applying to this position you will have an opportunity to your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA .
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. About the job Responsibilities 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.
