Data Scientist II, Operations Technology Solutions, Global Workforce Management

Description

Join Amazon's Global Workforce Management team as a Data Scientist II and Model Enhancement Specialist, where you'll develop and validate advanced forecasting models that optimize staffing for one of the world's largest operations networks. In this role, you'll apply cutting‑edge machine learning and statistical modeling techniques to solve complex workforce optimization challenges across global regions. This position offers the opportunity to drive technical innovation in workforce analytics, collaborate with cross‑functional data teams, and directly impact strategic planning decisions for Operations Technology Solutions.

• Validate and enhance forecasting models while developing specialized workforce analytics for field operations
• Create advanced scenario modeling algorithms and support model expansion to new organizations with technical expertise
• Collaborate with data teams to develop validation frameworks and establish technical metrics for model performance
• Design and implement specialized forecasting components with clear performance metrics and implementation paths
• Drive technical innovation in workforce analytics and mentor junior team members on advanced analytical approaches
• Travel up to 10% to support model implementation and stakeholder collaboration

As the Model Enhancement Specialist, you'll begin your day reviewing model performance metrics and validation results across multiple workforce forecasting systems. You'll collaborate with data engineering teams to refine algorithms, then work with business stakeholders to translate complex technical concepts into actionable insights. Your afternoons might involve developing new scenario modeling approaches in Python or R, conducting statistical analysis to improve forecast accuracy, or presenting technical findings to OTS Leadership.

You'll regularly work with cross‑functional teams to integrate analytical solutions, validate model outputs against real‑world outcomes, and identify opportunities for technical innovation in workforce optimization.

The Global Workforce Management (GWM) team is at the forefront of applying advanced analytics and machine learning to workforce optimization  develop and maintain sophisticated, data‑driven forecasting models that predict demand volume and staffing scenarios for Operations Technology Solutions (OTS) and expanding organizations across global regions. Our work directly enables strategic planning and operational decisions that impact millions of customer orders. The team values technical excellence, innovation in analytical methodologies, and collaborative problem‑solving.

We're expanding our modeling capabilities to new business units and exploring cutting‑edge approaches in AI and machine learning, making this an exciting time to join and shape the future of workforce analytics at Amazon.

• 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
• Master's 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…