Associate Data Scientist, Causal Machine Learning, Decision Science Systems, gReach Program (Japanese, English)
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Google welcomes people with disabilities.
This role is exclusively for people who have a Japanese government-issued disability certificate.
Minimum qualifications:
- Bachelor's degree in Statistics, Economics or Econometrics, Mathematics, Computer Science, Data Science or a related quantitative field, or equivalent practical experience.
- Ability to communicate in Japanese and English fluently to support client relationship management in this region.
Preferred qualifications:
- Master's degree or PhD in Statistics, Econometrics, Computer Science or a related quantitative field.
- Experience writing academic papers.
About the job
The gReach Program is a 12 months paid upskilling program for industry professionals with disabilities where participants will receive on-the-job-training and work on a range of projects. The goal of the gReach program is to provide participants from under-represented groups with the opportunity to grow their career as professionals.
Expect to spend about 70–80% of your time on data science development and coding. The other 20–30% goes to effectiveness insights, documentation and education. Over the 12 months, you'll follow a structured learning path. It starts with causal inference fundamentals and builds up to owning a pipeline feature end to end, from design through testing and release.
Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems--from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can--changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.
Responsibilities
- Develop and maintain doubleml-pipeline (Python). Design, implement, test and document new features and improvements with a senior data scientist’s support. Examples include input validation (such as column-name and data-type checks), normalization options, CATE function settings and a CATE-based budget optimization module. Contribute through code reviews and versioned package releases.
- Build a simulated data repository. Create and curate semi-synthetic datasets with known ground truth including confounding, latent confounders, mediators, adstock and saturation effects, time-varying parameters and regional heterogeneity. Use them to benchmark estimation accuracy (for example R², SMAPE and confidence-interval coverage).
- Contribute to research and publications. Run literature reviews and simulation experiments. Draft methodology sections for papers submitted to US and EU academic and technical conferences, such as the American Causal Inference Conference (ACIC).
- Create education material. Build example notebooks (Colab or Jupyter), onboarding documentation and codelab or hackathon content for Googlers and external partners.
- Support case studies. Run the pipeline on aggregated, privacy-safe data across promotions, retail media, brandformance and YouTube creator use cases. Where relevant, combine the results with Meridian (MMM) and experiments such as GeoX.
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