Principal Applied Scientist - Selling Partner Selection Success

Description

Are you passionate to join an innovative team of scientists and engineers who use machine learning and AI techniques to create state-of-the-art solutions to help seller succeed on Amazon? The Selling Partner Selection Success org is looking for a Principal Applied Scientist to lead us on our mission to provide selection success support on Amazon, and empower sellers to grow their business and provide a great customer experience.

As a Principal Applied Scientist on our team of scientists and engineers, you will have opportunities to create significant impact on our systems, our business and most importantly, our customers as we take on challenges that can revolutionize the e-commerce industry. You will identify specific and actionable opportunities to solve business problems, propose state-of-the-art solutions and collaborate with engineering, and business teams for future innovation. You need to be a great translation between ambiguous business domains and rigorous scientific solutions, an expert at inventing and simplify, and a good communicator to surface insights and recommendations to audiences of varying levels of technical sophistication.

We are building the next generation of selling on Amazon, and our mission is simple: every seller, across every product, should have access to high-quality, personalized guidance that helps them grow. This is a rare chance to build a directly seller-facing AI experience from the ground up. There is no playbook to follow, so you will bring creativity, scientific rigor, and strong business judgment to define what great looks like.


Key job responsibilities
- Set the technical vision for seller growth recommendations and drive significant business impact
- Design and build LLM-powered and agentic AI solutions that deliver personalized, high-quality guidance to every seller
- Advance recommendation science to surface the right opportunities for each seller, and define how recommendation quality and seller trust are measured
- Work backwards from seller needs to create science that is rigorous, explainable, and designed for a customer-facing experience
- Use Amazon's large-scale business data to uncover opportunities and automate and optimize key processes
- Partner closely with engineering, product, and business leaders to take models from research to real-time production
- Translate ambiguous business problems into clear science strategies, and communicate insights to audiences at every level
- Mentor and raise the bar for scientists across the team


A day in the life
You will lead the science behind seller growth recommendations: using data, LLMs, and agentic AI to give sellers guidance they can act on and trust. You will work backwards from the seller, designing science that is rigorous, explainable, and built for a customer-facing experience. You will own the problem end to end, from defining what a high-quality recommendation is, to earning seller trust, to scaling across a wide and evolving tech stack. Your work will directly shape how sellers grow their businesses on Amazon.

Basic Qualifications

- PhD in computer science, machine learning, engineering, or related fields
- 8+ years of applied research experience
- Experience translating real business problems into state-of-art production systems at scale
- Deep expertise and familiarity with the scientific foundation and latest development
- Excellent programming skills in Python and deep learning frameworks (PyTorch, TensorFlow)

Preferred Qualifications

- Demonstrated leadership in building and scaling agentic AI applications in production
- A history of building and scaling world-class teams and mentoring senior ICs to achieve career breakthrough

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.