Data Scientist, Security Issue Management
About the role
The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards.
Responsibilities
Design and run rigorous experiments to evaluate and improve security tooling performance, builder experience, and adoption across hundreds of thousands of builders, multiple security tools, and diverse business verticals.
Lead the end-to-end lifecycle of data science and ML models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout.
Conduct online and offline analyses to measure the real-world impact of security tooling improvements beyond adoption metrics, including downstream effects on vulnerability resolution, builder productivity, and organizational security posture.
Develop and deploy production-grade machine learning and statistical models using Python, SQL, and related tools to automate insights, detect patterns, and drive decision-making across STF's security tool ecosystem.
Perform large-scale exploratory data analysis on builder feedback, ticket resolution, tool usage, and customer satisfaction data to uncover patterns, identify opportunities, and inform product and tooling decisions.
Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels, including STF leadership metric reporting and customer satisfaction publications.
Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues.
Requirements
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)
Qualifications
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
Effective communication skills, including written and verbal communication
Skills
Python
SQL
Machine Learning
Data Analysis
Research Papers
Benefits
Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance, 401(k) matching, paid time off, and parental leave.
Pay
Base salary range: $136,000.00 - $184,000.00 USD annually
Schedule
Full-time