Applied Scientist II, Automated Profitability

Applied Scientist II, Automated Profitability

Interested in driving thought leadership by building machine learning solutions that will derive actionable insights about the complex economy of Amazon’s retail business? Whether you’re passionate about building highly scalable and reliable systems or a scientist who likes to solve novel business problems, the Automated Profitability AI team is the place for you. We use machine learning, generative AI, causal inference, and econometric/economic modeling to analyze Amazon’s relationships with vendors, and recommend actions that generate mutual growth.

We are an interdisciplinary team of applied scientists, engineers, and economists building solutions to solve some of the toughest business problems at Amazon.

As an Applied Scientist, you bring structure to ambiguous business problems and use science, logic, and practical experience to decompose them into scalable solutions. You use your broad experience with a diversity of machine learning methods to determine the right approach to solve complex business problems that can have humans-in-the-loop and may be data sparse. You provide feedback to other scientists on the team, help them define problems and solutions, and mentor junior scientists.

You’re committed to on‑going learning and can quickly acquire the knowledge you need to address novel problems. You work effectively with science, engineering, economics and business teams.

• Apply machine learning and modeling techniques including deep learning, generative AI, reinforcement learning, and causal inference methods to automate negotiation processes and drive business growth for Amazon and its vendors.
• Work with business subject‑matter experts (SMEs) to develop models that support human decision making during complex business negotiations.
• Design experiments and metrics that can quantify the impact of models on business outcomes.
• Collaborate with engineering teams to design and implement software solutions for science problems.
• Contribute to Amazon and broader research communities by producing publications.

The Automated Profitability Team transforms how Amazon manages and optimizes procurement. We leverage AI to empower Worldwide Stores and Vendors to make “best‑in‑class” economic decisions with contextually relevant insights, and recommendations that align with broader business objectives.

• 3+ years of building models for business application experience.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top‑tier peer‑reviewed conferences or journals.
• 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 using Unix/Linux.
• Experience in professional software development.

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 (Use the "Apply for this Job" box below).

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. As a total compensation company, Amazon's package may include other elements such as sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well‑being.

We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

Posted: February 25, 2026 (Updated about 3 hours ago)