Data Scientist , Reactive Transfers

AI in this role

fine-tuning

The Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a Data Scientist with strong analytical and communication skills to join our team. SCOT manages Amazon's inventory under uncertainty of demand, pricing, promotions, supply, vendor lead times, and product life cycle. We optimize complex trade-offs between customer experience, inventory costs, fulfillment costs, fulfillment center capacity, etc. We develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and other data from our product catalog in order to automatically act on millions of dollars’ worth of inventory weekly and establish plans for tens of thousands of employees. As a Data Scientist, you will contribute to the research community, by working with other scientists across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers both internally and externally.

Key job responsibilities
Major responsibilities include:
- Analysis of large amounts of data from different parts of the supply chain and their associated business functions
- Improving upon existing machine learning methodologies by developing new data sources, developing and testing model enhancements, running computational experiments, and fine-tuning model parameters for new models
- Formalizing assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
- Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our research, as well as sharing insights and recommendations
- Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms

A day in the life
As a Data Scientist in SCOT, you will be tasked to understand and work with cutting edge research to enable the implementation of sophisticated models on big data. As a successful data scientist in the SCOT team, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.

About the team
The Supply Chain Optimization Technologies (SCOT) organization owns Amazon’s global inventory management systems: we decide what, when, where, and how much we should buy to meet Amazon’s goals and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large-scale , inventory and supply chain planning and optimization systems. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the efficiency of Amazon's inventory investment and placement decisions.

The Fulfillment Optimization team is focused on using cutting edge science to improve customer outcomes and transform our logistics, along with machine learning, and scalable distributed software in the cloud that automates and optimizes shipments to customers under the uncertainty of demand, pricing and supply. When customers place orders, our systems use real time, large scale optimization techniques to optimally choose from where to ship and how to consolidate multiple orders so that customers get their shipments on time or faster with the lowest possible transportation costs. One of our core responsibilities is to leverage big data to identify key patterns of success and failure, identify the areas we need to focus on first, understand root cause(s) that triggered failure, and to build predictive models that will help fix the most impactful problems. The amount of data, the variables that come into play, and diversity of customers and locations make this role very challenging and also fun. It's great for those who love solving problems, especially when dealing with a lot of ambiguity and asking lots of smart questions that will lead to the discovery of universal concepts, and truly innovative ML solutions that continuously improve the customer delivery experience.

We are seeking an outstanding Data Scientist to join the team. Amazon.com has culture of data-driven decision-making, and demands data analysis that is timely, accurate, and actionable. If you join the Amazon.com’s SCOT FO, your work will have an immediate influence on day-to-day decision making at Amazon.com.
As a Data Scientist you will be working in one of the world's largest and most complex data warehouse environments. You will work with Product Managers, ML Scientists, Senior Executives to gather requirements and apply data science methodologies to solve complex business problems. You should have deep expertise in analyzing huge data sets and using complex data sets from multiple domains. You should be expert at designing and implementing solutions that use a range of data science methodologies to automate data analysis or to solve complex business problems. You should be able to work with business customers in a fast paced environment understanding the business requirements and implementing reporting solutions.
This opportunity is perfect for highly motivated and talented data scientists who want to apply and grow their technical depth and breadth while defining and driving key aspects of the customer experience on Amazon.com.

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
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment

Preferred qualifications

- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, TX, Austin - 136,000.00 - 184,000.00 USD annually

How we rate this

Data Scientist , Reactive Transfers at Amazon rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Data Scientist , Reactive Transfers is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $136k-$184k a year.
The role is based in US, TX, Austin.
The job was posted on October 6, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine Tuning

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Fine Tuning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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