Sr. Data Scientist, Field Engineering

Amazon • Seattle, WA

1d • Hybrid

About The Position

AWS Infrastructure Services is responsible for the design, planning, delivery, and operation of all AWS global infrastructure, ensuring customers have continuous access to the innovation they rely on. The Data Center Field Engineering Team specifically owns the lifecycle of AWS data center mechanical and electrical infrastructure, focusing on root cause analysis of failures, capacity and availability improvement, and optimization of the existing fleet. As a Senior Data Scientist on the Field Engineering Portfolio team, you will apply advanced analytical and machine learning capabilities to develop scalable models and data-driven frameworks that measure, predict, and improve fleet performance across the global AWS data center fleet. You will be a strong communicator, translating complex findings into clear recommendations for leadership, and will collaborate cross-functionally with various teams to ensure data science solutions are grounded in operational reality and drive measurable impact. You will partner with engineering teams and program managers to define metrics, identify performance gaps, and build the analytical infrastructure needed to support strategic decisions at hyper-scale, operating in ambiguous, fast-moving environments where speed of insight is crucial. The ideal candidate possesses strong problem-solving skills, stakeholder communication abilities, and the capacity to balance technical rigor with delivery speed and customer impact. You will develop scalable analytical approaches to evaluate performance, identify insights, design experiments, and shape the development roadmap, building cross-functional support to assess business problems and define metrics for iterative scientific solutions.

Requirements

• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 4+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
• Master's degree in a quantitative field such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Engineering, or Computer Science; OR Bachelor's degree and 8+ years of professional experience in a quantitative role.

Nice To Haves

• 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
• Experience documenting modeling for technical and business leaders
• Experience working with data engineers and business intelligence engineers collaboratively
• Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
• Experience developing operational processes and data insights
• Experience with anomaly detection, predictive maintenance, or reliability modeling in industrial or infrastructure contexts.

Responsibilities

• Develop and maintain scalable models and analytical frameworks to measure and predict data center fleet performance, including availability, efficiency, and reliability KPIs across the global AWS infrastructure portfolio.
• Apply advanced statistical and machine learning techniques to extract actionable insights from complex, large-scale operational datasets generated by data center systems (power, cooling, controls, etc.).
• Partner with Field Engineers, Operations, and Portfolio Managers to identify high-impact opportunities for capacity and availability improvement, translating engineering domain knowledge into quantitative problem formulations.
• Design and implement end-to-end data science workflows — from data acquisition and cleaning through model development, validation, and production deployment — enabling repeatable, scalable analysis.
• Formalize assumptions about how data center systems are expected to perform and develop methods to systematically identify deviations, root causes, and high-ROI improvement opportunities.
• Build self-service datasets, dashboards, and reporting mechanisms that provide Field Engineering leadership with real-time visibility into fleet health and portfolio performance.
• Prepare narratives and data-driven recommendations for executive leadership that articulate decision points relative to fleet investment, risk trade-offs, and strategic priorities.
• Collaborate with applied science, software engineering, and data engineering teams to ensure models integrate seamlessly with upstream and downstream systems.

Benefits

• 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
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior