Senior Applied Scientist, JP Seller Services

Amazon is hiring a Senior Applied Scientist, JP Seller Services in Tokyo, Japan. Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this applied scientist role: the job is building AI systems.
Tools and skills the posting cites include Python, PyTorch and AWS.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Designs and builds AI-native science platforms and multi-agent frameworks to automate hypothesis testing and predictive analytics.

PythonPyTorchAWS

machine-learningcausal-inferencegenerative-aimulti-agent-systems

We are seeking an exceptional Applied Scientist to join our JP Seller Services team, where you will reimagine how science analysis and modeling are conducted across the organization through an AI-native approach. In this role, you will design and build intelligent systems that enable any team member to validate business hypotheses with scientific rigor in hours rather than months. You will architect production-grade platforms spanning multi-agent AI frameworks, causal inference automation, generative AI, and simulation engines that democratize advanced analytics at scale. Your work will fundamentally transform how the teams generate, test, and deploy data-driven recommendations, scaling rigorous science solutions for every decision-maker to solve customer problems. The ideal candidate combines deep expertise in scientific analysis such as causal inference, machine learning, and AI system design with the vision to rethink the entire science lifecycle from hypothesis to deployment.

At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture.

Key job responsibilities
- Lead the design and development of AI-native science platforms that automate the end-to-end lifecycle from hypothesis formulation through causal analysis, model validation, and deployment into production systems.
- Design and build shared knowledge infrastructure (feature stores, experiment registries, model leaderboards) that enables cumulative organizational learning, where every validated insight accelerates future analyses.
- Design and implement evaluation frameworks, including Seller simulations, that enable teams to validate model quality and test interventions against synthetic populations before live deployment.
- Drive integration with downstream systems to close the gap between validated insights and seller-facing actions, ensuring science outputs reach the people and systems that serve customers.
- Collaborate with cross-functional partners (product managers, category leaders, marketing managers, economists, and data scientists) to identify high-impact business problems and translate them into scalable scientific solutions.

Basic qualifications

- 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

Preferred qualifications

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Speak, write, and read fluently in Japanese at a business level or above (N1+)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Senior Applied Scientist, JP Seller Services at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Applied Scientist, JP Seller Services is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in JP, 13, Tokyo.
The job was posted on July 11, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningCausal InferenceGenerative AIMulti Agent SystemsPythonPyTorchAWS

Also common in applied scientist jobs at this level

Deep learning LLM NLP Reinforcement learning TensorFlow Spark

Pay

Typical applied scientist job: $169k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where causal inference was part of your work. What did you do?
Tell me about a project where generative ai was part of your work. What did you do?
Tell me about a project where multi agent systems was part of your work. What did you do?
Walk me through how you've used Python in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine learning, Causal Inference, Generative AI, Multi Agent Systems, and Python. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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