The JP Retail Science team is looking for an Senior Applied Scientist to expand our efforts in vendor tooling.
To help vendors grow their business, Amazon provides multiple programs such as deals, ads, etc. In this position, you will be expected to research, design and build new models to evaluate the downstream impact of those programs, and provide recommendation on the most appropriate programs depending on the vendor need. It will require working in the areas of ML and causal inference for downstream impact estimation. The ideal candidate will have knowledge of at least one of ray, spark or rapidsai framework to accelerate model training. A background in causal inference (e.g. Double ML) is a plus but not required. This is the ideal role if you are excited about leveraging science for tangible business impact.
You will work within an international team of scientists and engineers, all based in Tokyo, Japan. We are a team that thrives on growth, both personal and professional. Engage in academic collaborations, spark innovation in hackathons, and expand your horizons with conference visits.
Key job responsibilities
As a Senior Applied Scientist, your responsibilities will be:
- Work closely with other scientists and engineers to review and improve your model design proposals.
- Partner with product managers and other business stakeholders, documenting and explaining your progress in business reviews, and being the technical voice in charge of your product.
- Spot opportunities for innovation and scientific publications, and publish to internal or external conferences.
- Be active in the community, participating in science education/growth activities.
- Keep up to date with scientific development in the field.
About the team
JP Retail Science is a team of Applied Scientists, Science Managers, and Business Intelligence Engineers. The team's charter is to develop science-based models to help all Amazon vendors to maximize their growth. From our base office in Tokyo, Japan, we build for all vendors worldwide, and collaborate with other science teams in Europe and US.
Because we are not tied to a specific technology, such as Search or Alexa, our projects and the science required change dynamically depending on the vendor needs. In the past we have worked on initiatives drawing from multiple disciplines, including causal inference, LLM, forecasting, and optimization.
A large fraction of the team consists of former academic researchers, and we maintain that culture through collaboration with universities, exchange programs, and conference participation. - PhD, or Master's degree and 5+ years of building machine learning models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl- PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
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