Business Data Scientist, Applied Machine Learning, GCS

Google's leadership team hand-picks thorny business challenges, 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 GCS Data Science team is working on challenging yet interesting problems for Google's Global Business Organization (GBO). Our goal is to build efficient and scalable ML models that help small and midsize businesses around the world grow their business, leveraging the power of Google solutions.

In this role, you will be passionate about solving problems with the latest research in applied deep learning, causal inference and measurement theory. We work with product teams to understand their objectives, business requirements and constraints, and key metrics. We propose, build, evaluate and debug machine learning models and algorithms; we integrate our pipelines, models and predictions into production serving systems.

The US base salary range for this full-time position is $138,000-$198,000 + 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 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.
• Stay current with the latest academic research in Causal ML and Econometrics, proactively prototyping new methods to improve the precision of our impact estimates.
• Distill 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.

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA.