Data Scientist, SCOT (Supply Chain Optimization Technologies) RoW Tech
Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Sep 21, 2026. Amazon scores A on the Alion truth index.
SCOT's RoW Regional team is seeking an experienced and motivated Data Scientist to help optimize, enhance, and scale Supply Chain decision algorithms in countries beyond US/EU. The focus will be on enhancing and also building new solutions solving for forecasting, buying, placement and inventory health management issues in 10 stores.
You will work closely with Product Managers Tech, BIEs, SDEs, Supply Chain Managers, and other Scientists across US and EU to build scalable, high insight- high impact products and own improvements to business outcomes within your area, enabling execution of WW and local solutions for next generation Supply Chain systems.
You will leverage your expertise in modeling, maths, algorithms and more importantly AI to analyze diverse and large datasets to uncover insights and build working models that help minimize dependence on manual supply chain decisions within the vast landscape and selection in which Amazon operates. Areas of work would include: 1) Building OR, RL, Agentic AI based models for automated supply chain optimization decisions; integrating with the central tech solutions owned by SCOT WW, 2) Creating decision frameworks to choose between multiple models to solve for a given supply chain problem, 3) Build visibility, audit and decision frameworks that help operate complex and nuanced supply chains across 10 countries.
Key job responsibilities
You will be responsible for researching, experimenting and analyzing predictive and optimization models. You will work on ambiguous and complex business and research science problems with large opportunities. You are an analytical individual who is comfortable working with cross-functional teams and systems. Significantly AI Savvy. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams.
Key job responsibilities
- Interact with product managers to understand their business requirements and operational processes.
- Frame business problems into scalable solutions.
- Adapt existing and invent new techniques for solutions.
- Be the proponent of using latest techniques in data science and leveraging AI to fasten experimentation and model launch.
- Gather data required for analysis and model building.
- Collaborate with product and engineering teams to design rigorous experiments to evaluate the impact of new features or algorithms.
- Create and track accuracy and performance metrics.
- Prototype models by using high-level modeling languages such as R or in software languages such as Python.
- Familiarity with transforming prototypes to production is preferred.
- Create, enhance, and maintain technical documentation.
- Represent the team and contribute to science forums within Amazon
About the team
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, Amazon’s Supply Chain Optimization Technology (SCOT) organization is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment working with smart & passionate team members. (Learn more about SCOT: http://bit.ly/amazon-scot)
Basic qualifications
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
Preferred qualifications
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
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