Amazon.com Services LLC
Data Science, Science, amazon security
DataScientist,SecurityIssueManagement
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Scientist, Security Issue Management at Amazon.com Services LLC. Skills: AI/ML solutions, ML models, Experimentation. Design AI/ML solutions. Implement AI/ML solutions”
What You'll Achieve.
Ship secure code faster; Maintain highest security standards; Enhance builder experience; Enhance builder productivity
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
2+ years of data scientist experience, 3+ years of data querying languages experience, 3+ years of scripting languages experience, 3+ years of statistical/mathematical software experience, 3+ years of machine learning/statistical modeling experience
Nice to Have
Ph. D. in Science, Technology, Engineering, or Mathematics (STEM), Knowledge of machine learning concepts, Experience in Python, Experience in Perl, Experience in another scripting language, Experience in a ML role, Experience in a data scientist role, Experience with large technology company, Experience in defining GenAI model performance benchmarks, Experience creating GenAI model performance benchmarks, Experience working on multi-team projects, Experience working on cross-disciplinary projects, Experience applying quantitative analysis, Experience solving business problems, Experience making data-driven business decisions, Experience effectively communicating complex concepts
What You'll Do.
Design AI/ML solutions
Implement AI/ML solutions
Improve builder experience
Balance theoretical knowledge
Balance practical implementation
Collaborate with cross-functional teams
Deliver impactful solutions
Evaluate algorithm performance
Evaluate model performance
Establish best practices for ML experimentation
Establish best practices for ML evaluation
Establish best practices for ML development
Establish best practices for ML deployment
How You'll Work.
Team & Collaboration
Cross-functional teams; Diverse team; Other teams
Communication Scope
Written communication; Verbal communication
Full Job Description
Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact? 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. As a Data Scientist on our Security Tooling team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality. Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity. Key job responsibilities • Design and implement novel AI/ML solutions for complex security challenges and improve builder experience • Balance theoretical knowledge with practical implementation • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work
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