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Applied Scientist II - P13N, Shopping Intent

Amazon

  • Seattle, WA
  • 30+ days ago
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    Skills

    • Algorithmsunmatched
    • Analysis Skillsunmatched
    • Architectural Designunmatched
    • Big Dataunmatched
    • Business Modelunmatched
    • Business Solutionsunmatched
    • C++ Programming Languageunmatched
    • Computer Visionunmatched
    • Customer Experienceunmatched
    • Customer Support/Serviceunmatched
    • Data Analysisunmatched
    • Data Miningunmatched
    • Data Modelingunmatched
    • Data Structuresunmatched
    • Distributed Computingunmatched
    • Entrepreneurshipunmatched
    • Javaunmatched
    • Large-Scale Systemsunmatched
    • Leading Edge Technologyunmatched
    • Linux Operating Systemunmatched
    • Machine Learningunmatched
    • Mentoringunmatched
    • Model Validationunmatched
    • Natural Language Processing (NLP)unmatched
    • Parallel Computingunmatched
    • Predictive Modelingunmatched
    • Process Improvementunmatched
    • Python Programming/Scripting Languageunmatched
    • Software Developmentunmatched
    • Software Engineeringunmatched
    • Unix Operating Systemsunmatched
    • eCommerceunmatched

    Description

    Description Are you interested in big data, machine learning, LLM, and product recommendations? If so, Amazon's Personalization team might be the right place for you. About our organization: We are part of Amazon's Personalization organization, a high-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large-scale distributed systems. We run global experiments and our work has revolutionized e-commerce with features such as "Compare with similar items", "Keep shopping for ...", "Customers who bought this item also bought", and, "Frequently bought together" among others. Amazon's internal surveys regularly recognize us as one of the best organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continuous opportunity to learn and grow. About you: You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state-of-art technologies (e.g., LLM, Machine Learning, NLP, and Computer Vision) to build the next-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of products used every day by people you know. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes Design, development and evaluation of highly innovative models for predictive learning Work closely with software engineering teams to drive model implementations and new feature creations Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Research and implement novel machine learning and statistical approaches Mentor junior scientists; review their work and provide feedback About the team Our mission is to delight every Amazon customer with a personalized shopping experience. We achieve our mission through investments in large-scale machine learning and distributed system solutions with the purpose of delivering the future of shopping on Amazon. Our solutions help customers explore product categories, discover high quality products that meet their needs, and provide most relevant information to help customers make confident shopping decisions. We are seeking an Applied Scientist to make step function improvements in creating a delightful shopping experience. Basic Qualifications - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing Preferred Qualifications - Experience using Unix/Linux - Experience in professional software development 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 - 142,800.00 - 193,200.00 USD annually

    Numbers & Facts

    LocationSeattle, WA
    IndustryOther/Not Classified
    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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