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Director, Data Science, Amazon Customer Service Network Solutions

Amazon.com Inc

  • Seattle, WA
  • 2 days ago
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    Skills

    • Artificial Intelligence (AI)unmatched
    • Artificial Intelligence (AI) Agentsunmatched
    • Automationunmatched
    • Business Intelligenceunmatched
    • Capacity Managementunmatched
    • Cross-Functionalunmatched
    • Customer Experienceunmatched
    • Customer Relationsunmatched
    • Customer Service Toolsunmatched
    • Customer Support/Serviceunmatched
    • Data Analysisunmatched
    • Data Qualityunmatched
    • Data Scienceunmatched
    • Demand Forecasting/Planningunmatched
    • Economicsunmatched
    • Establish Prioritiesunmatched
    • Incident Managementunmatched
    • Intelligence Agenciesunmatched
    • Intelligent Networkunmatched
    • Interoperabilityunmatched
    • Leadershipunmatched
    • Network Architecture/Engineeringunmatched
    • Network Designunmatched
    • Network Routingunmatched
    • Operational Measurementunmatched
    • Product Engineeringunmatched
    • Production Systemsunmatched
    • Quantitative Analysisunmatched
    • Resource Managementunmatched
    • Standards Developmentunmatched
    • Team Playerunmatched
    • Transportation Routingunmatched

    Description

    We are seeking a seasoned executive leader to join our Network Solutions team within Amazon Customer Service, as Director, Data Science. In this role you will build and lead a data, analytics, and measurement science function that orchestrates Amazon"s Customer Service full network - across our human-assisted and customer-facing automation AI-enabled channels - as a single intelligent system. Network Solutions owns demand forecasting, network planning, routing, real-time observability, and workforce strategy, and is building the next generation of AI-enabled planning systems with intelligent and adaptive capabilities that consider humans and AI-agents, simultaneously.

    You will lead a multi-disciplinary team - scientists, data engineers, business intelligence engineers, and analysts - who will build the data strategy these new systems require and the measurement science that reshapes how every network decision is made. At the core is a unified measurement framework that measures the value of experiences across customers, associates, and our business, drawing on causal inference, economics, operations, and behavioral science. You will create the underlying logic for how we plan capacity, route customers, develop associates, and allocate resources - and serve as an interface and work closely with partner teams within Customer Service, including the CS-wide Data Intelligence organization.

    The ideal candidate brings deep cross-disciplinary experience in data science, measurement, and operations - a track record of building and leading data and insights organizations from 0-to-1, the scientific depth to drive novel measurement frameworks, and the rare ability to blend quantitative rigor with an understanding of human systems - developing the right logic to drive decision-making at scale.

    Key job responsibilities

    • Build and lead the Network Solutions data and analytics organization: provide unified leadership across business intelligence engineering, data engineering, analytics, and reporting; recruit, grow, and retain a team spanning measurement scientists, data engineers, and analysts, building the function from 0-to-1
    • Drive the measurement science and decisioning core: develop the causal inference-based framework that quantifies value creation across multiple dimensions; establish methodology standards, signal definitions, and utility function design that give Network Solutions a unified view of performance
    • Own the Network Solutions data foundation: own critical signal pipelines, govern data quality, and build a composable data architecture that all Network Solutions product and science teams build on; ensure measurements are interoperable across planning, routing, incident management, and workforce systems
    • Integrate measurement science into production systems: demonstrate the shift to value-informed decision-making; partner with product and engineering teams to embed measurement frameworks into planning, routing, and resource allocation
    • Interface with Amazon CS and company-wide data, analytics, and science organizations: represent Network solutions as a peer partner to central data infrastructure, customer experience measurement, and applied science functions; consolidate Network Solutions data demand into a clear, prioritized voice
    • Drive adoption of data standards and measurement frameworks: translate complex models and multi-dimensional utility functions into intuitive, actionable insights for non-technical stakeholders; establish shared measurement standards that teams across the organization can build on
    • Develop the long-term data and science roadmap: anticipate future needs as the organization scales; identify opportunities to extend measurement capabilities beyond Network Solutions

    About the team

    Network Solutions sits at the intersection of customer experience and associate experience within Amazon Customer Service, owning the logic and infrastructure that determines how those two sides are balanced, served, and optimized together. On one side is the customer: their experience, their journey, and the AI automation that increasingly shapes both. On the other side is the associate: their work, their well-being, and the conditions that make them effective. Network Solutions sits at the center, owning demand forecasting, network planning, routing and matching, real-time observability, and workforce strategy - including the long-term design of the network itself as the balance between AI and human engagement continues to evolve.

    We are building the next generation of systems that will transform how Customer Service operates using intelligent, adaptive capabilities that learn and improve continuously.

    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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