Senior Applied Scientist, Strategic Sourcing Science

Amazon Supply Chain Optimization Technology (SCOT) Optimal Sourcing Systems (OSS) is looking for an Senior Applied Scientist to help build the next generation strategic sourcing systems. The OSS owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units.

Our goals are to increase reliable access to supply, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon.

This role specifically requires a candidate with expertise in optimization, inventory control theory, and reinforcement learning, alongside the standard qualifications for an Amazon Applied Scientist III. The selected candidate will primarily work to lead the design and implementation of science solutions for the Optimal Sourcing Supply Chain project, which evaluates and compares various retail sourcing and vendor inbound strategies to identify the most effective approaches.

Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. You will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them.

• Set the scientific strategic vision for the team. Lead problem decomposition and roadmap development.
• Identify and frame research challenges in ambiguous problem areas; invent novel methodologies to address them. Distinguish between problems requiring novel solutions versus those addressable with existing approaches.
• Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs.
• Set an example with exemplary scientific analyses; maintainable, well-tested code; and simple, effective solutions.
• Drive the design of scientifically-complex software solutions, personally writing critical-path code that embodies scientific novelty. Deploy novel models into production with a track record of impactful delivery.
• Develop reusable science components that resolve architecture deficiencies; set standards and drive adoption of state-of-the-art techniques.
• Influence team business and engineering strategies.
  
  - Communicate effectively with stakeholders to drive alignment and build consensus.
• Foster collaborations between scientists across Amazon researching similar problems. Proactively resolve endemic issues where the team's technologies bottleneck other teams.
• Actively engage in the development of others, both within and outside the team.
• Participate in the science hiring process and engage with the broader scientific community through publications, presentations, and patents.

• 5+ years of applied research experience
• PhD in operations research, applied mathematics, theoretical computer science, plus 5+ years industry experience
• Knowledge of supply chain management, inventory control, stochastic optimization
• Proven track record of launching novel scientific solutions in highly ambiguous domains — from problem formulation through research, prototyping, and production deployment
• Proficiency in coding (e.g., Python) with experience developing production-quality scientific software
• Technical depth in one or more of: mathematical optimization, causal inference, stochastic modeling, or sequential decision-making

• Publication record in operations research, machine learning, or a related quantitative field
• Experience designing and analyzing experiments in operational or supply chain settings
• Experience with deep learning frameworks (e.g., PyTorch, Tensor Flow)

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