From Amazon's posting. “We” and “our” refer to the employer.
We are looking for an Applied Science Manager III to lead a team of applied scientists building next-generation AI-powered seller risk detection systems. This role sits at the intersection of deep technical science, strategic planning, and cross-organizational partnership. GRIP Science serves a complex stakeholder landscape: enforcement teams, policy owners, marketplace operations, peer science orgs, and senior leadership, each with distinct timelines and priorities.
The right leader for this role will navigate that landscape with clarity, balancing urgent operational needs against the long-term scientific investments required to stay ahead of adversarial actors at scale.
You will independently own the vision, roadmap, and execution for a critical detection surface, defining what to build, what to defer, and how to measure success. You will set and maintain a high bar for scientific rigor while cultivating a team culture that rewards creative problem-solving and principled risk-taking.
Key job responsibilities
Strategy & Stakeholder Partnership
Define the scientific vision and multi-year roadmap for your team's detection domain, ensuring alignment across a broad set of stakeholders with competing priorities and timelines
Develop strong working relationships with enforcement, policy, operations, and engineering partners; serve as the trusted scientific voice in cross-functional discussions
Exercise judgment in sequencing work. Distinguish between high-urgency requests that warrant immediate response and those better addressed through systematic, durable solutions
Influence org-level planning (OP1/OP2) through compelling written narratives and data-driven proposals that articulate both near-term deliverables and long-term scientific bets
Proactively communicate trade-offs to leadership and stakeholders, building trust through transparency about what your team will and will not prioritize in a given cycle
Science & Technical Leadership
Lead a team building state-of-the-art detection models that leverage modern AI techniques, including deep learning, foundation models, and generative AI, to identify novel abuse patterns at scale
Set and uphold a high standard for scientific methodology: model validation, experimentation rigor, reproducibility, and interpretability of results
Evaluate end-to-end scientific proposals for soundness, scalability, and production readiness; know when to push for deeper investigation and when to ship
Foster a culture of scientific curiosity and invention. Encourage the team to challenge assumptions, explore out of the box approaches, and invest in research that creates step-function improvements rather than incremental gains
Identify where emerging AI capabilities (e.g., large language models, multimodal reasoning, self-supervised learning) can be applied to unlock new detection signals or fundamentally reshape existing pipelines
Make informed trade-offs between model sophistication, delivery timelines, and operational sustainability
Execution & Operational Excellence
Scope scientific projects, set priorities, and drive schedules; readjust decisively when incoming signals warrant a change in direction
Structure work to reduce exposure to classic failure modes: ambiguous requirements, untested assumptions, under-instrumented models
Establish mechanisms and metrics to continuously measure model effectiveness, enforcement precision, and downstream business outcomes
Drive production readiness: monitoring, alerting, on-call practices, and rapid response for AI/ML systems processing millions of daily decisions
Team Building & Development
Build and lead a high-performing team of applied scientists; attract and develop talent that combines technical depth with ownership mindset
Invest in the growth of individual scientists through deliberate project assignment, regular coaching, and actionable feedback tied to career progression
Create an environment where scientists have the space to do deep work, the support to take calculated risks, and the recognition when they deliver
Manage performance across the full spectrum. Accelerate top performers toward promotion readiness while addressing gaps constructively and with clear expectations
About the team
GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate state-of-the-art ML and AI models that identify and block bad sellers across Amazon's 24 global marketplaces. Our systems cover the full seller journey, from registration through every action on the marketplace, and score holistic risk across all active sellers.
We push the frontier of adversarial detection by combining large-scale deep learning, graph-based reasoning, generative AI, and real-time inference to outpace bad actors who continuously evolve their tactics. This is one of the most technically demanding and high-impact applied science problem spaces at Amazon. Your models directly shape marketplace integrity for hundreds of millions of customers worldwide.
Basic Qualifications
3+ years of scientists or machine learning engineers management experience
Knowledge of ML, NLP, Information Retrieval and Analytics
Ph.D. in computer science, machine learning, engineering, or related fields, or PhD and 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
1+ years of scientists or machine learning engineers management experience
Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
Experience working and communicating with multiple stakeholders, C-level executives and cross-functional teams
Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
Preferred Qualifications
Experience building machine learning models or developing algorithms for business application
Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
Equal Opportunity
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 - 183,800.00 - 248,700.00 USD annually
About this listing
LAYIQ is an independent job-discovery service. This listing does not imply a partnership with or endorsement by the employer. Review the original posting for current details and availability.
