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Applied Scientist , Personalization & Ranking

Amazon.com Inc
  • Seattle, WA
    3 days ago

    Job Description

    Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon.

    As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems.

    Key job responsibilities

    • Using Amazon's large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website.
    • Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods;
    • Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;
    • Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses;
    • Effectively communicate technical and non-technical ideas with teammates and stakeholders;
    • Stay up-to-date with advancements and the latest modeling techniques in the field;

    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

    Skills

    • Analysis Skillsunmatched
    • Artificial Intelligence (AI)unmatched
    • Communication Skillsunmatched
    • Customer/Consumer Behaviorunmatched
    • Data Analysisunmatched
    • Data Setsunmatched
    • Deep Learningunmatched
    • Machine Learningunmatched
    • Modeling Languagesunmatched
    • Problem Solving Skillsunmatched
    • Product Designunmatched
    • Reinforcement Learningunmatched
    • Retailunmatched

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