Data Scientist II, WW DSP Analytics

Overview

Job :  |  Services LLC

The WW DSP Analytics team is a centralized analytics organization within Amazon's Last Mile Delivery Service Partner (DSP) program. We build best-in-class solutions that enable data-driven decision making across our global DSP ecosystem. Our team partners with internal stakeholders, DSP owners, and cross-functional teams to deliver insights that drive operational excellence, business growth, and the success of small business owners in Last Mile delivery.

Our work directly impacts customer experience, driver and station associate experience, DSP success, and Amazon's sustainable growth. The goal of Amazon’s DSP organization is to exceed the expectations of our customers by ensuring that their orders, no matter how large or small, are delivered as quickly, accurately, and cost effectively as possible. To meet this goal, Amazon is continually striving to innovate and provide best in class delivery experience through the introduction of pioneering new products and services in the last mile delivery space.

Come join us and help us make history!

• Develop Science Solutions for DSP Capacity Planning & Business Health:
  Design and implement data science solutions that optimize Delivery Service Partner (DSP) capacity allocation and business health measurement across the global DSP network. Leverage deep expertise in mathematical optimization and causal inference to identify opportunities for improving capacity planning models, volume share calibration methodologies, and business health measurement systems that drive partner sustainability.
• Analyze Sentiment Risks & Business Health Metrics:
  Analyze sentiment risks and enhance algorithms that support DSP program management, including business health indicators, capacity reliability models, and partner viability frameworks that inform intervention strategies.
• Translate Business Requirements into Mathematical Models:
  Demonstrate strategic thinking by translating high-level DSP capacity planning and business health improvement requirements into optimization formulations and predictive models, and applying them to quantify return on investment for policy changes and network interventions.
• Build Production-Scale Analytics:
  Contribute to the development and deployment of scalable data models, dashboards, and automated reporting systems that enable self-service analytics for DSP stakeholders and surface business health signals at scale.
• Accelerate GenAI Footprint:
  Partner with Data Engineers to expand our GenAI tools and improve developer productivity, while raising the bar on data quality and enabling intelligent automation across capacity planning workflows.
• Conduct Independent Data Analysis:
  Mine and analyze complex datasets across multiple domains, business health metrics, financial data, capacity signals, and operational data, using programming and statistical tools to generate actionable insights.
• Thrive in a Collaborative Environment:
  Excel in a fast-paced analytics organization that encourages collaborative and creative problem-solving. Measure and communicate analytical risks, constructively critique peer work, and align research focuses with DSP capacity planning strategic needs.
• Partner Cross-Functionally:
  Work closely with Business Intelligence Engineers, capacity planning teams, and DSP stakeholders to define KPIs, validate analytical approaches, and ensure insights drive meaningful outcomes.

We are the WW DSP Analytics team with the vision to enable data, insights and science driven decision-making. We have exceptionally talented and fun loving team members. In our team, you will have the opportunity to dive deep into complex business and data problems, drive large scale technical solutions and raise the bar for operational excellence. We love to share ideas and learning with each other.

We believe in promoting and using ideas to disrupt the status quo.

• Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or experience working in Science, Technology, Engineering, or Mathematics (STEM)
• 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
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 2+ years of data scientist experience
• 2+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
• 1+ years of working with or evaluating AI systems experience
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• Experience effectively communicating complex concepts through written and verbal communication

• Ph.D. in econometrics,…