Sr. Applied Scientist, AppStar Data Analytics & Engineering

Overview

Are you passionate about using science to make the digital world more secure? The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Are you passionate about using science to make the digital world more secure? The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Scientis

What you'll do

• Are you passionate about using science to make the digital world more secure?
• The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Are you passionate about using science to make the digital world more secure?
• The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Scientist III to invent and build ML-driven systems that fundamentally change how Amazon identifies, prioritizes, and mitigates application security risk at scale.
• Our team sits at the intersection of data science, machine learning, and security operations.
• We build the intelligence layer that powers Amazon's application security programs: risk-scoring models that rank tens of thousands of applications, graph-based systems that map security context across architectures, and analytics platforms that drive data-informed decisions for security leadership.
• This is science with direct, measurable impact on Amazon's security posture.
• As an Applied Scientist III, you will lead the invention and delivery of novel ML solutions for complex, ambiguous problems in the security domain.
• You will work with large-scale datasets spanning application metadata, code signals, vulnerability findings, and organizational context to develop models that help Amazon focus security resources where they matter most.
• Key job responsibilities - Lead the design, development, and deployment of ML models and scientific solutions for application security prioritization, complexity scoring, and risk assessment - Frame ambiguous security problems into well-defined scientific challenges, proposing novel approaches when existing methodologies are insufficient - Architect and implement production-grade ML pipelines (feature extraction, model training, scoring, deployment) on AWS services (S3, Glue, SageMaker, Neptune) - Develop and extend graph-based models that capture security-relevant relationships between applications, services, teams, and vulnerabilities - Drive the team's scientific agenda by proposing new research initiatives, conducting experiments, and iterating on models using rigorous evaluation methodologies - Partner with security engineers, data engineers, and TPMs to translate model outputs into actionable intelligence for security review programs - Establish and raise the bar for scientific rigor: peer review code and designs, set best practices for experimentation, and document findings for reproducibility - Publish results internally and externally at peer-reviewed venues when appropriate About the team The Data Analytics & Engineering (DNA) team is a small, high-impact group within Amazon's Application Security organization.
• We build ML models, graph-based systems, and analytics platforms that determine how Amazon prioritizes security coverage across tens of thousands of applications.

Requirements

• Practical experience with Go, Rust, AWS, RAG.
• Experience level: 3+ yrs.
• Strong written and verbal communication in English.
• Comfortable working on-site in New York, New York, USA.

Skills