Amazon Scholar

The FBA Science team builds the scientific capabilities required to manage Amazon supply chain and fulfillment resources effectively at scale, while ensuring FBA sellers build and grow their businesses. Growing scope, diversity, and scale of the FBA business problems require our team to have significant breadth and depth across scientific knowledge and tool sets. We are problem driven and therefore organize around key work streams. This enables us to build and implement science solutions that are not limited to one specific discipline but instead are at the intersection of operations research (OR), machine learning (ML), economics, statistics, data analytics, and management science. We are also a centralized science team to ensure solutions consider the interdependency of end-to-end FBA systems, products, and policies as well as the coordination with SCOT, AFT, and DEX systems. Such inter-system integration must incorporate FBA's unique supply patterns brought by, for example, different inventory ownership, the large seller base and the large volumes of tail ASINs. Given these considerations, we organize our deliverables across seven workstreams: capacity management, inventory network management, forecasting and analytics, system and policy performance, seller recommendations and assistance, seller experience, and inventory defects and reimbursements. Each of these workstreams employs a wide range of scientific methodologies to solve the problems.

We are open to hiring candidates to work out of one of the following locations:

Virtual Location - TX | Virtual Location - USA

Basic Qualifications

PhD in a relevant field or related discipline

5+ years of relevant work or academic experience

Experience leading technical research projects with multiple stakeholders

Current affiliation with an academic or research institution

Preferred Qualifications

Recognized expert in the external community in an applied science discipline and routinely applies knowledge from other disciplines

Publications at top-tier, peer-reviewed conferences and/or journals

Broad knowledge of applied mathematics and foundational understanding of algorithms and computational complexity

Expert-level research analysis and technical leadership capabilities

Expert knowledge in modeling and performance, operationalization, and scalability of scientific techniques and establishing decision strategies

Ability to independently lead research development and analysis in a fast-paced environment

Proven track record of innovation in creating novel technologies and advancing the state of the art

Publications at top-tier, peer-reviewed conferences and/or journals

Exceptional verbal and written communication and consensus-building skills with both technical and non-technical audiences

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit

Pursuant to the Los Angeles Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.