# Associate Data Scientist, Causal Machine Learning, Decision Science Systems, gReach Program (Japanese, English) Source: https://realanalystjobs.com/job/associate-data-scientist-causal-machine-learning-decision-science-systems-greach-bd4d6cdb Associate Data Scientist, Causal Machine Learning, Decision Science Systems, gReach Program (Japanese, English) at Google, based in Tokyo, Japan. Posted on 13 November 2026, read from Google's own careers page (Google), which RealAnalystJobs re-checks daily and removes the role when it closes. Updated 15 November 2026. ## Key facts - Company: [Google](https://realanalystjobs.com/companies/google) - Location: Tokyo, Japan - Posted: Nov 13, 2026 - Employment type: Full time - Source: Google's careers page (Google), re-checked daily Skills: [Python](https://realanalystjobs.com/skills/python) ## Visa sponsorship The posting does not say. Google had H-1B petitions approved for 6,163 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/google#h1b-record) ## About this role 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. As an Associate Data Scientist in the gReach Program, you'll join the decision science system (Open-source) development workstream. You'll do research and engineering under a senior data scientist’s direct supervision. Your main focus will be improving and extending doubleml-pipeline: its causal ML algorithms, a simulated-data benchmarking repository, and how it connects with AI agents. You'll also help publish the methodology at academic and technical conferences. 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. • 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. 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. Description from Google's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/associate-data-scientist-causal-machine-learning-decision-science-systems-greach-bd4d6cdb) and use Apply now there to reach the employer's application. The listing is re-checked against Google's careers page daily and removed when it closes. ## Google 57 open analyst roles on Real Analyst Jobs. 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