Senior Data Scientist, Amazon

• 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 experience
• Experience with statistical models e.g. multinomial logistic regression
• Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
• Experience building and deploying production machine learning models (classification, regression, time-series forecasting, anomaly detection)

Desirable

• The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure
• We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision
• We are seeking a Senior Data Scientist to own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions
• You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide
• Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling
• Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions
• Develop anomaly detection systems to identify emerging patterns in operational data, enabling proactive incident prevention
• Create econometric and statistical frameworks to measure the impact of operational interventions and optimize resource allocation decisions
• Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift)
• Design and conduct experiments to empirically validate model performance and operational impact
• Develop data quality monitoring frameworks and establish standards for operational data integrity
• Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate complex analytical insights into actionable strategies
• Communicate findings, model performance, and strategic recommendations to senior leaders through written documents and presentations
• Mentor junior data scientists and engineers on the team; raise the bar on scientific rigor and analytical best practices
• Contribute to the team's multi-year science roadmap, identifying new opportunities to apply ML/AI (including Generative AI and agentic architectures) to operational challenges