Key job responsibilities
* Build the intelligence behind AI-first security products: Design, train, and ship ML models that power agentic systems, anomaly detection, threat classification, and automated response - all running across multi-cloud environments.
* Own the full science lifecycle: From problem framing and data exploration through model development, evaluation, production deployment, and monitoring. You build it, you ship it, you run it.
* Build with AI to build AI: Use agentic coding tools, LLM-powered workflows, and experimental AI tooling to accelerate every phase of your work; from EDA to feature engineering to model iteration. Multiply your velocity and raise the bar for what one scientist can deliver.
* Power agentic architectures: Develop the models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready.
* Prototype rapidly and validate with customers: Turn hypotheses into prototypes in days, not quarters. Iterate based on real customer signal and ship what works.
* Partner across disciplines: Work directly with SDEs, applied scientists, security researchers, PMs, and UX designers to turn ambiguous problems into shipped solutions. Small team means short lines between you and the decision.
* Communicate with impact: Translate complex modeling results into clear recommendations for engineers, product leaders, and senior executives. Influence direction with data.
* Raise the science bar: Contribute to technical and science reviews, mentor teammates, and champion AI-first development practices. Help shape the science culture of a fast-growing team from the ground floor.
A day in the life
No two days look the same on this fast-growing, AI-first team. You might start your morning reviewing evaluation results from overnight model training runs, then dive into building a RAG pipeline or tuning a multi-agent orchestration loop. Before lunch, you're pair-prompting with an agentic coding assistant to stand up a new feature pipeline. In the afternoon, you join a design session with senior and principal scientists and engineers where your ideas carry weight regardless of title. You own science problems end to end, ship using the latest AI-assisted workflows, and see your models reach production fast. This is where builders thrive.
BASIC QUALIFICATIONS
- 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 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
PREFERRED 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 applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience in security, anomaly detection, or other low-signal/high-stakes ML domains
- Experience optimizing AI integrations or building agentic development processes (e.g., Ralph loop, parallelized agentic development)
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, TX, Austin - 136,000.00 - 184,000.00 USD annually
