Business Data Scientist, Applied Machine Learning, Gcs

Google Google · Big Tech · Mountain View, CA +2

This role focuses on building and evaluating ML models for business growth, specifically using causal inference and deep learning techniques. The responsibilities include designing, developing, and validating causal inference models, partnering on A/B tests, prototyping new methods, communicating technical findings to executives, and establishing monitoring systems for deployed models. The role aims to integrate ML models into production serving systems and ensure their ongoing accuracy.

What you'd actually do

  1. 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.
  2. Partner with business teams to design and execute A/B tests, defining the sample sizes, power analyses, and success metrics required for valid results.
  3. Stay current with the latest academic research in Causal ML and Econometrics, proactively prototyping new methods to improve the precision of our impact estimates.
  4. Distill highly technical methodologies into clear, prescriptive business narratives for non-technical executive audiences.
  5. Establish comprehensive monitoring systems to track model performance, detect data drift, and ensure the ongoing accuracy of deployed measurement frameworks.

Skills

Required

  • Python
  • R
  • SQL
  • analytics
  • statistical analysis
  • causal inference models

Nice to have

  • PhD in a quantitative discipline
  • publications
  • working with technologies
  • driving a project from an experimental idea to a proof-of-concept to a launched product feature

What the JD emphasized

  • latest research in applied deep learning
  • causal inference
  • measurement theory
  • evaluate and debug machine learning models and algorithms
  • integrate our pipelines, models and predictions into production serving systems
  • Design, develop, and validate robust causal inference models
  • Stay current with the latest academic research in Causal ML and Econometrics
  • Establish comprehensive monitoring systems to track model performance, detect data drift, and ensure the ongoing accuracy of deployed measurement frameworks.

Other signals

  • ML models
  • applied deep learning
  • causal inference
  • measurement theory
  • production serving systems