Applied Scientist, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention

Amazon is hiring an Applied Scientist, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention in Seattle, United States. It pays $143k-$193k a year and 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.
The posting mentions Python, Machine learning and Small Language Models among its tools and skills.
Level classes it as a senior role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Design and build predictive risk detection models using advanced AI and machine learning to prevent marketplace abuse at scale.

PythonMachine learningSmall Language Models

machine-learningrisk-modelinggraph-analysisfraud-detection

We are seeking an exceptional Applied Scientist, Global Selling Partner Risk Intelligence and Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to identify compromised accounts, fraudulent ownership transfers, identity manipulation, and coordinated bad actor networks across the entire seller lifecycle, from registration through ongoing account management.

You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and account relationship graphs to build predictive systems that stay ahead of evolving adversarial tactics.

Key job responsibilities
* Design and build predictive risk detection models using advanced AI techniques, including graph-based and network analysis methods, to proactively identify bad actors and prevent marketplace abuse at scale

* Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels

* Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience

* Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas

* Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness

About the team
GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate ML models that identify and block bad sellers across Amazon's 24 global marketplaces. Our models cover the full seller journey, from the moment they register through every action they take on the marketplace, and we score holistic risk across all active sellers.

Basic qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in development in the last 3 years
- Knowledge of deep learning, machine learning and statistics
- Track record of translating ambiguous business problems into well-defined ML solutions

Preferred qualifications

- Experience in professional software development
- PhD
- Experience with adversarial ML, fraud detection, or abuse prevention systems
- Familiarity with graph-based learning methods for detecting coordinated networks
- Experience modeling temporal and sequential data (e.g., account behavior over time, event sequences)
- Experience building models that operate on multi-modal data (text, structured, behavioral signals)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

How we rate this

Applied Scientist, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention 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

Applied Scientist, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $143k-$193k a year.
The role is based in US, WA, Seattle.
The job was posted on July 15, 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 learningRisk ModelingGraph AnalysisFraud DetectionPythonSmall Language Models

Also common in applied scientist jobs at this level

Generative AI NLP Reinforcement learning TensorFlow Spark AWS

Pay

This job starts at: $143k
Typical applied scientist job: $167k

This posting starts 15% below the typical applied scientist job. 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 risk modeling was part of your work. What did you do?
Tell me about a project where graph analysis was part of your work. What did you do?
Tell me about a project where fraud detection 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, Risk Modeling, Graph Analysis, Fraud Detection, 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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