Job Description

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

• 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.

About the job

The Google Customer Solutions (GCS) Data Science team is working on challenging yet interesting problems for Google Global Business Organization (GBO). Our vision is to build efficient and scalable ML models that help small and midsize businesses around the world to grow their business leveraging the power of Google solutions.

We look for candidates who are passionate about solving real-world 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

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 rigorous 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 and black box model outputs 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.