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Data Scientist I, FMA

Amazon.com Inc

  • Seattle, WA
  • 13 days ago
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    Skills

    • Algorithmsunmatched
    • Analysis Skillsunmatched
    • Building Systemsunmatched
    • Communication Skillsunmatched
    • Customer Experienceunmatched
    • Customer/Consumer Behaviorunmatched
    • Data Scienceunmatched
    • Data Setsunmatched
    • Identify Issuesunmatched
    • Leadershipunmatched
    • Machine Learningunmatched
    • Performance Analysisunmatched
    • Performance Managementunmatched
    • Performance Modelingunmatched
    • Predictive Modelingunmatched
    • Pricingunmatched
    • Product Engineeringunmatched
    • Quality Managementunmatched
    • Salesunmatched
    • Software Engineeringunmatched

    Description

    Every time a customer clicks "Add to Cart" on Amazon, they"re trusting us to have already answered a deceptively hard question: out of hundreds of competing offers for the same product, which one is actually the best choice for them right now? Our team owns the algorithm that answers that question - billions of times a day, across every product on Amazon.

    As a Data Scientist on this team, you"ll help make that algorithm smarter. You"ll work on the models and signals that rank and surface offers in real time - weighing price, seller performance, fulfillment speed, customer trust signals, and more. The work is technically deep: you"re not running one-off analyses, you"re building systems that operate at internet scale and directly affect what customers buy and how sellers compete.

    You"ll run experiments, develop predictive models, and partner with engineers and product managers to turn your findings into features that ship. If you enjoy problems where getting the answer right really matters - for customers, for sellers, and for the business - you"ll find a lot to work on here.

    Key job responsibilities

    Build and own machine learning models that rank and recommend offers to customers across Amazon"s product catalog, from feature engineering through production deployment

    Design and analyze A/B experiments to measure the impact of algorithm changes on customer experience, seller competition, and business outcomes

    Mine large-scale datasets to identify patterns and signals - seller behavior, pricing dynamics, fulfillment performance - that improve how we predict the best offer for each customer

    Translate ambiguous business questions into well-defined data science problems, and communicate findings clearly to engineers, product managers, and leadership

    Partner with software engineers to operationalize models at scale, ensuring they perform reliably under high-traffic, low-latency conditions

    Monitor model performance over time, diagnose degradation, and iterate to keep ranking quality high as the marketplace evolves

    A day in the life

    You"ll spend your time building and iterating on models that rank offers for hundreds of millions of Amazon shoppers - checking experiment results, digging into datasets to find better signals, and working with engineers to get models into production.

    Day to day, you"ll partner with product managers and engineers to turn business questions into data science problems: Why is this ranking signal underperforming? What"s driving a shift in customer behavior? Is this change actually better for customers?

    Your work directly shapes what customers see and how sellers compete - at a scale few roles can match.

    About the team

    We own one of the most consequential algorithms at Amazon - the system that decides which offer a customer sees when they"re ready to buy. It"s a small surface area with enormous impact, and we take that responsibility seriously.

    Our team brings together scientists, engineers, and product managers who are genuinely obsessed with getting the ranking right - for customers and for sellers. We move fast, run lots of experiments, and debate ideas openly. We care about doing the science rigorously, shipping things that matter, and not taking shortcuts that erode customer trust.

    If you like working on hard problems with people who hold each other to a high bar, you"ll fit in here.

    Numbers & Facts

    LocationSeattle, WA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

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