Applied Scientist II, SCOT-Optimal Sourcing Systems, Buying Science Team

Amazon is looking for an Applied Scientist to help build the next generation of sourcing and vendor experience systems. The Optimal Sourcing Systems (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.

As an Applied Scientist 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.

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. Some critical research areas in our space include modeling buying decisions under high uncertainty vendors behavior and incentives supply risk and enhancing visibility and reliability of inbound signals.

Key job responsibilities
- 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 teams 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.

- PhD in operations research applied mathematics theoretical computer science or equivalent or Masters degree and 4 years of machine learning statistical modeling data mining and analytics techniques experience
- Knowledge of supply chain management concepts - forecasting planning sourcing optimization and logistics or equivalent
- Experience building and deploying machine learning or optimization models for business applications at scale
- Proficiency in Python with experience developing production-quality scientific software
- Technical depth in one or more of: mathematical optimization causal inference sequential decision-making (RL/MDP) or stochastic modeling

- Publication record in operations research machine learning or a related quantitative field
- Experience with optimization solvers (e.g. Gurobi OR-Tools) or reinforcement learning frameworks (e.g. RLlib Stable Baselines)
- Experience with deep learning frameworks (e.g. PyTorch TensorFlow)
- Experience designing and analyzing experiments in operational or supply chain settings

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

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