Staff Business Data Scientist, Google Customer Solutions

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 7 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• Experience with building machine learning models using libraries such as TensorFlow and PyTorch.
• Experience in designing and implementing causal inference methods (e.g., A/B testing, difference-in-differences, synthetic control, instrumental variables) is essential.

Preferred qualifications:

• 9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

About the job

Google's leadership team hand-picks thorny business issues, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations. The US base salary range for this full-time position is $197,000-$291,000 + 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.

Responsibilities

• Design, develop, and validate causal inference models (e.g., Synthetic Control, Difference-in-Differences, Double Machine Learning) to accurately isolate and measure the true incremental impact of GCS programs and interventions.
• Serve as the team's subject matter expert on Causal Inference and advanced measurement, consulting with Product, Engineering, and Business leaders to translate questions into testable hypotheses and rigorous designs.
• Stay current with the latest academic and industry research in Causal ML, Deep Learning, and Econometrics, proactively proposing and prototyping new methods.
• Partner with stakeholders to design rigorous A/B tests and randomized control trials (RCTs) when feasible, and define the metrics and success criteria for each study.