Data Scientist, WW DSP Analytics

The WW DSP Analytics team is a centralized analytics organization within Amazons 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 Amazons sustainable growth.

We are seeking a passionate Data Scientist with strong machine learning and analytical skills to join our team. You will work on challenging problems in the delivery planning space, applying data science rigor to generate actionable insights that support DSP performance measurement and continuous improvement.

Develop Science Solutions for DSP Performance: Design and implement data science solutions to optimize Delivery Service Partner (DSP) operations, capacity planning, and performance measurement across the global DSP network

Apply Advanced Machine Learning Techniques: Leverage solid research experience in Machine Learning and statistical modeling to identify opportunities for improving DSP analytics, forecasting models, and performance measurement systems

Optimize DSP Program Policies and Sentiment Risks: Analyze sentiment risks and enhance existing algorithms that support DSP program management, including scorecard metrics, capacity reliability models, and performance evaluation frameworks

Analyze Business Requirements with Return on Investment (ROI) calculation: Demonstrate superior logical thinking by quickly approaching large, ambiguous problems, translating high-level DSP program requirements into mathematical models, and applying models to predict the return on investment.

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

Accelerate GenAI footprint: Partner with Data Engineers to expand our GenAI tools and improve developer productivity along with raising the bar on data quality.

Conduct Independent Data Analysis: Mine and analyze complex datasets across multiple domains (performance metrics, ficial data, operational data) using programming and statistical analysis 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 program strategic needs

Partner Cross-Functionally: Work closely with Business Intelligence Engineers, program teams, and DSP stakeholders to define KPIs, validate analytical approaches, and ensure insights drive meaningful business outcomes 2+ years of data scientist experience

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

1+ years of guiding and coaching a group of researchers experience

1+ years of working with or evaluating AI systems experience

1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience

Masters degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Experience applying theoretical models in an applied environment Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)

Knowledge of machine learning concepts and their application to reasoning and problem-solving

Experience in Python, Perl, or another scripting language

Experience in a ML or data scientist role with a large technology company

Experience in defining and creating benchmarks for assessing GenAI model performance

Experience working on multi-team, cross-disciplinary projects

Experience applying quantitative analysis to solve business problems and making data-driven business decisions

Experience effectively communicating complex concepts through written and verbal communication