Data Scientist, Security Issue Management

Amazon Amazon · Big Tech · Seattle, WA · Data Science

This role focuses on building Agentic AI solutions using LLMs and machine learning to identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle. The Data Scientist will build ML models to enhance builder experience and productivity, study developer behavior, and measure the impact of security tooling.

What you'd actually do

  1. identify builder bottlenecks and pain points across the software development lifecycle
  2. design and apply experiments to study developer behavior
  3. measure the downstream impacts of security tooling on engineering velocity and code quality
  4. build state-of-the-art ML models to enhance builder experience and productivity

Skills

Required

  • 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources 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)

Nice to have

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
  • Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
  • Experience working with or evaluating AI systems
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • building Agentic AI solutions
  • using LLMs and machine learning
  • state-of-the-art ML models
  • security excellence

Other signals

  • building Agentic AI solutions
  • using LLMs and machine learning
  • identify builder bottlenecks
  • automate security workflows
  • optimize the software development lifecycle
  • build state-of-the-art ML models
  • study developer behavior
  • measure the downstream impacts of security tooling