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Data Scientist, PPE Product IntelligenceAmazon • United States
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Data Scientist, PPE Product Intelligence

Amazon
  • US
    United States
  • US
    United States
Postuler Maintenant

À propos

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 work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships — this is where you do it.
Basic Qualifications
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
2+ years of data scientist 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
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, WA, Seattle - 136,000.00 - 184,000.00 USD annually
  • United States

Compétences linguistiques

  • English
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