Data Science Assistant Jobs in Austin, TX (NOW HIRING)

What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI at the same time. 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.
We're looking for a Senior Data Scientist who operates at the intersection of applied ML, agentic AI, and security; and who can set technical direction across ambiguous, undefined problem spaces. You won't just build models; you'll decide which problems are worth solving, architect the scientific approach for an entire product area, and raise the bar for how the team applies science. You'll partner with senior and principal engineers, applied scientists, security researchers, and PMs, and your judgment will shape roadmaps, not just deliverables. This is a role for someone who thrives in ambiguity, influences without authority, and turns "too ambitious" into shipped reality.
Key job 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.
A day in the life
No two days look the same on this fast-growing, AI-first team. You might start your morning setting direction in a roadmap review; making the call on which science investments will have the biggest customer impact and then dive into architecting an evaluation framework that the whole team will build on. Before lunch, you're pair-prompting with an agentic coding assistant to validate a new approach, then unblocking a teammate stuck on a thorny modeling problem. In the afternoon, you lead a design session with senior and principal scientists and engineers, then distill it into a crisp recommendation for senior leadership. You own ambiguous problems end to end, define how the team works, and see your decisions ripple across the product. This is where builders who want to lead with science come to do their best work.
About the team
Why AWS?
Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.
Hybrid Work
We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
BASIC 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
PREFERRED QUALIFICATIONS
- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team
- Master's degree in a quantitative field, or PhD
- Experience leading the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection)
- Experience with end-to-end ownership of major project deliverables
- Experience as a mentor, tech lead or leading an engineering team
- Experience working on multi-team, cross-disciplinary projects
- Experience communicating complex ideas to technical and non-technical audiences
- Experience defining and scaling agentic development processes (e.g., Ralph loop, parallelized agentic development) across a team or org
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
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 - 159,200.00 - 215,300.00 USD annually