Google

Runs Search and YouTube and builds Gemini AI

Associate Data Scientist - Causal Machine Learning, Decision Science Systems

Applied Machine LearningPosted on Nov 13

Full-timeTokyo, JapanNo salary listedIn-person

About the Job

Requirements

A 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 fluently in Japanese and English to support client relationship management in the region.

Responsibilities

Develop, maintain, test, and document doubleml-pipeline features and improvements with support from a senior data scientist, including input validation, normalization options, CATE function settings, and a CATE-based budget optimization module.
Contribute through code reviews and versioned package releases.
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 simulated datasets to benchmark estimation accuracy, including R², SMAPE, and confidence-interval coverage.
Conduct literature reviews and simulation experiments, and draft methodology sections for papers submitted to academic and technical conferences in the United States and Europe, such as ACIC.
Create example notebooks in Colab or Jupyter, onboarding documentation, and codelab or hackathon content for Googlers and external partners.
Run the pipeline on aggregated, privacy-safe data across promotions, retail media, brandformance, and YouTube creator use cases.
Where relevant, combine pipeline results with Meridian (MMM) and experiments such as GeoX.
Conduct research and engineering under a senior data scientist's direct supervision, focusing on improving and extending doubleml-pipeline, its causal machine-learning algorithms, a simulated-data benchmarking repository, and its connections with AI agents.
Help publish methodology at academic and technical conferences.
Follow a structured 12-month learning path, from causal inference fundamentals to owning a pipeline feature end to end, from design through testing and release.

Desired Qualifications

A master's degree or PhD in Statistics, Econometrics, Computer Science, or a related quantitative field.
Experience writing academic papers.

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.

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.

Google welcomes people with disabilities.

This role is exclusively for people who have a Japanese government-issued disability certificate.

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.

About the Company

Google

Runs Search and YouTube and builds Gemini AI

Google runs the world's most used search engine and free services such as YouTube, Android, and Gmail, and it builds the Gemini AI models that work inside them. In 2017, its researchers invented the transformer, the neural network design behind ChatGPT and most other AI chatbots. People use these services without paying, and Google earns most of its money from ads placed beside search results and YouTube videos. Businesses rent computing power and Gemini models through Google Cloud, and Google also sells subscriptions and Pixel phones. Larry Page and Sergey Brin founded Google in 1998 as Stanford PhD students; it is based in Mountain View, California, United States, and is the main business of parent company Alphabet.

Company size: 10,001+
Company stage: IPO
Headquarters: Mountain View, CA
Founded: 1998

Recently Posted Jobs

Explore more opportunities at Google

See more jobs