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Data Scientist, PPE Product Intelligence
Amazon
On-sitemidPosted June 2, 2026
About the Role
Lead the full lifecycle of price estimation models from research and experimentation to production, including designing experiments, conducting statistical analysis, and deploying models.
Requirements
Design, run, and analyze experiments at scale, develop and deploy production-grade statistical models for price estimation, and conduct large-scale exploratory data analysis.
Full Job Description
Amazon's Price Perception and Evaluation team is seeking a driven Data Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to build and scale an advanced self-learning scientific price estimation and product understanding system, regularly generating fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide.
The Data Scientist will work closely with other research scientists, applied scientists, and SDEs to design and run experiments, conduct statistical analysis, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers.
If you are a scientist who wants to work at the frontier of time series research, at a scale no academic lab or startup can match, and see your work deployed to real-world impact — this is the team for you.
Key job responsibilities
- Design and run rigorous experiments at scale to evaluate and improve foundation model performance across hundreds of millions of products, geographies, and business verticals
- Lead the end-to-end lifecycle of evaluation models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout
- Conduct online and offline labs to measure the real-world impact of model improvements beyond accuracy, including downstream supply chain, inventory, and financial outcomes
- Develop and deploy production-grade statistical models using Python, Scala, SQL, and related tools
- Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform model development
- Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels
A day in the life
A day in the life
No two days look the same, but most will involve some combination of deep technical work, cross-functional collaboration, and scientific thinking at a scale you won't find anywhere else.
You might start the morning reviewing the results of an experiment running across hundreds of millions of products — analyzing whether a new foundation model variant is improving generalization on cold-start items, or whether a novel data generation approach is meaningfully shifting forecast quality. You'll dig into the numbers, form a hypothesis, and design the next iteration.
Later in the day, you could be in a stakeholder review, walking business and engineering partners through a set of launch metrics — explaining not just forecast accuracy, but the downstream supply chain and financial impact your model is driving. Getting a model to production at Amazon requires rigor: you'll define success criteria, run online and offline labs to validate real-world impact, and build the case for sign-off across technical and business stakeholders.
You'll write code — Python, Scala, SQL — to process and analyze data at a scale most scientists never encounter. You'll collaborate closely with scientists, engineers, and business teams, and contribute to research that has a real chance of being published and advancing the field.
The Data Scientist will work closely with other research scientists, applied scientists, and SDEs to design and run experiments, conduct statistical analysis, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers.
If you are a scientist who wants to work at the frontier of time series research, at a scale no academic lab or startup can match, and see your work deployed to real-world impact — this is the team for you.
Key job responsibilities
- Design and run rigorous experiments at scale to evaluate and improve foundation model performance across hundreds of millions of products, geographies, and business verticals
- Lead the end-to-end lifecycle of evaluation models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout
- Conduct online and offline labs to measure the real-world impact of model improvements beyond accuracy, including downstream supply chain, inventory, and financial outcomes
- Develop and deploy production-grade statistical models using Python, Scala, SQL, and related tools
- Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform model development
- Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels
A day in the life
A day in the life
No two days look the same, but most will involve some combination of deep technical work, cross-functional collaboration, and scientific thinking at a scale you won't find anywhere else.
You might start the morning reviewing the results of an experiment running across hundreds of millions of products — analyzing whether a new foundation model variant is improving generalization on cold-start items, or whether a novel data generation approach is meaningfully shifting forecast quality. You'll dig into the numbers, form a hypothesis, and design the next iteration.
Later in the day, you could be in a stakeholder review, walking business and engineering partners through a set of launch metrics — explaining not just forecast accuracy, but the downstream supply chain and financial impact your model is driving. Getting a model to production at Amazon requires rigor: you'll define success criteria, run online and offline labs to validate real-world impact, and build the case for sign-off across technical and business stakeholders.
You'll write code — Python, Scala, SQL — to process and analyze data at a scale most scientists never encounter. You'll collaborate closely with scientists, engineers, and business teams, and contribute to research that has a real chance of being published and advancing the field.