Applied Machine Learning Scientist for Business Growth

Google Inc.; New York (NY); On-site; USD 138,000 - 197,000; Full time

Benefits offered by this job

Equity

Bonus target

Job summary

Google's GCS Data Science team seeks a Business Data Scientist who builds scalable ML models to growth for small and midsize businesses worldwide. You will apply applied ML to measure impact and inform executive decisions within the Global Business Organization.

You will advance causal inference methods, translate insights into actionable business guidance, and collaborate with product teams to implement features and monitor outcomes at scale.

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.
• PhD in a quantitative discipline is preferred.

Responsibilities

• Design, develop, and validate robust causal inference models (e.g., Synthetic Control, Difference-in-Differences, Double Machine Learning) to isolate impact.
• Partner with business teams to design and execute A/B tests with appropriate sample sizes and power analyses.
• Track the latest academic research in Causal ML and Econometrics to improve impact estimates.
• Translate technical methodologies into clear business narratives for executives.
• Establish monitoring systems to track model performance and data drift.

Skills

Python

R

SQL

Education

Master's degree in a quantitative discipline

Tools

Causal ML techniques

Job description

Google's GCS Data Science team seeks a Business Data Scientist who builds scalable ML models to growth for small and midsize businesses worldwide. You will apply applied ML to measure impact and inform executive decisions within the Global Business Organization.

You will advance causal inference methods, translate insights into actionable business guidance, and collaborate with product teams to implement features and monitor outcomes at scale.