Senior Applied Scientist, SCOT OSS - Sourcing Execution & Performance

Description

Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, the Amazon Global Supply Chain Optimization Technology (SCOT) organization is for you. Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast?

Wondered where it came from and how much it cost Amazon? If so, the Amazon Global Supply Chain Optimization Technology (SCOT) organization is for you.

Watch this video to learn more about our organization, SCOT: (Use the "Apply for this Job" box below).-scot

We are the Optimal Sourcing Systems team (OSS) within SCOT and are looking for a Senior Applied Scientist to join us! OSS designs and builds systems that measure and manage Amazon’s supplier capabilities, identify and react to supply disruptions, and prioritizes inbound freight for our global network. OSS software is used by every country Amazon services, and is a critical link to ensuring Amazon offers the products our customers want, at the lowest possible cost.

This team under OSS orchestrates and tracks inventory movement into Amazon's network, maintains performance feedback loops, and ensures vendor compliance.

The Senior Applied Scientist, in partnership with the Product Management and Tech teams, will lead efforts in following areas:

• Provide technical leadership and mentorship to the Science team, setting the standard for methodological rigor, peer review, and innovation across all work streams
• Build solutions to enable collaborative inventory planning with vendors through agent to agent collaboration or humans-in-the-loop collaborative methods
• Pioneer Gen AI solutions for dispute evaluation and vendor coaching, defining the technical approach, evaluating model performance against business outcomes, and establishing responsible AI guardrails for production deployment
• Drive the full development cycle from whiteboarding new algorithmic approaches to production-scale deployments
• Collaborate with SDEs to build high-performance, distributed training and inference pipelines; translate complex scientific concepts into scalable, production-grade code

The ideal candidate is a seasoned scientist who thrives in ambiguous, high-impact problem spaces and brings the technical depth to independently structure and solve complex challenges across the supply chain. The successful candidate will be a person who has deep understanding about machine learning/reinforcement learning/GenAI models, enjoys and excels at diving into data to analyze root causes, and implementing long term solutions.

They can translate complex business logic into scalable models and communicate insights effectively to both technical and non-technical stakeholders. Keys to success in this role include exceptional Science depth and breadth, analytics, statistics, judgment, and communication skills. Experience with supply chain optimization, operations research, or vendor management systems is a plus.

Set the technical vision and drive best practices for the team's science solutions, including model evaluation frameworks, experimentation standards, code quality, and documentation

Mentor junior scientists and raise the bar across the organization

Lead cross-functional collaboration with product managers, science, and engineering teams to define problem frameworks, identify high-impact opportunities, and architect end-to-end model solutions for Sourcing Execution & Performance systems

Design and execute rigorous studies and predictive modeling pipelines on large-scale datasets and experiments, establishing methodological standards for statistical validity, reproducibility, and business interpretability

Partner with engineering to product ionize science workflows driving the automation of analysis processes, building scalable measurement solutions, and ensuring models are robust, monitored, and maintainable in production environments

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

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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