Senior Data Scientist, Enterprise Security Products
About the role
Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. This role is a chance to define and lead the science strategy for the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale.
Responsibilities
Set the science direction for a product area: Define the modeling strategy, scientific approach, and success metrics for entire categories of AI-first security capabilities, agentic systems, anomaly detection, threat classification, and automated response across multi-cloud environments.
Decide where science can move the needle and where it can't.
Own the hardest, most ambiguous problems: Take on undefined, open-ended challenges where the path isn't clear, the data is messy or scarce, and the stakes are high. Frame the problem, choose the approach, and bring others along.
Build with AI to build AI and define how the team does it: Drive adoption of agentic coding tools, LLM-powered workflows, and experimental AI tooling across the science org. Establish the practices that multiply velocity for every scientist, not just yourself.
Architect agentic intelligence: Lead the design of models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready at scale. Own the science architecture decisions others build on.
Drive technical strategy across teams: Influence roadmaps, dive deep with senior and principal scientists and engineers, and align cross-functional partners around a shared scientific vision. Your recommendations shape what the team invests in next.
Prototype, validate, and scale: Turn ambiguous hypotheses into prototypes in days, validate with real customer signal, and chart the path from prototype to production system that runs reliably at Amazon scale.
Communicate to influence at the executive level: Translate complex modeling results and scientific trade-offs into clear recommendations for engineers, product leaders, and senior executives. Drive organizational decisions with data and earn trust across the company.
Raise the bar and grow others: Mentor data scientists and applied scientists, lead technical and science reviews, and champion AI-first development practices. Shape the science culture and hiring bar of a fast-growing team from the ground floor.
Qualifications
- 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
- Experience defining roadmap strategy and prioritizing deliverables for your team products
- Experience handling ambiguous or undefined challenges through strong problem solving abilities
- Bachelor's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
- Experience leading the design and deployment of ML or statistical models in large-scale production environments
- Experience with AI-assisted or agentic coding practices