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

Amazon; Baltimore (MD); On-site; USD 140,000 - 195,000; Full time

Job summary

Amazon is seeking an Applied Scientist II for the SCOT-Optimal Sourcing Systems within the Buying Science Team. You will design and deploy innovative optimization and ML solutions for sourcing and vendor experience across global supply chains.

You will collaborate with software engineers, product managers, and business teams to translate business problems into scalable technical solutions, with a focus on improving reliability and reducing end-to-end costs.

Qualifications

• PhD in OR/Applied Math/CS, or MS with 4+ years ML experience.
• Strong knowledge of supply chain concepts: forecasting, planning, sourcing, optimization.
• Experience building and deploying ML/optimization models at scale.

Responsibilities

• Lead problem decomposition and roadmap for OSS initiatives.
• Frame research challenges and develop novel methods.
• Prioritize between short-term and long-term business and tech needs.
• Write maintainable, production-quality scientific software.
• Deploy models to production with measurable impact.
• Partner with engineers and product teams to deliver solutions.
• Contribute to science hiring and publish patents or papers.

Skills

Python

Machine Learning

Optimization

Education

PhD in OR / Applied Math / CS, or MS with 4+ years ML experience

Tools

Gurobi

OR-Tools

PyTorch

TensorFlow

RLlib

Job description

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 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 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.

Basic Qualifications

• PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's 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

Preferred Qualifications

• 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.

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.

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