Data Scientist, Alexa Connections

Amazon Amazon · Big Tech · IN, TN +1 · Applied Science

This role focuses on the end-to-end development and deployment of machine learning and data science solutions for intelligent communication experiences within Alexa. The Data Scientist will design, build, and improve ML models for capabilities like prioritization and intent detection, analyze large datasets, conduct experiments, and contribute to the applied science roadmap, ultimately delivering AI-driven solutions to millions of customers.

What you'd actually do

  1. Partner with product, engineering, operations, and security teams to translate complex business problems into scalable, production-ready data science and ML solutions.
  2. Own the full lifecycle of model development, including exploratory analysis, model design, deployment, monitoring, and continuous improvement.
  3. Define and track success metrics, conduct rigorous analyses, and provide insights that guide product launches, feature improvements, and data-driven decision-making.
  4. Build data-driven business cases to prioritize science initiatives and demonstrate measurable impact of ML solutions.
  5. Develop ML-powered systems supporting key business areas.

Skills

Required

  • 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 creating or contributing to mathematical textbooks, research papers, or educational content experience

Nice to have

  • 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 working on multi-team, cross-disciplinary projects
  • 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

  • production-ready
  • full lifecycle of model development
  • rigorous analyses
  • measurable impact
  • ML-powered systems

Other signals

  • end-to-end development of machine learning and data science solutions
  • scalable ML models
  • advanced ML and statistical models
  • run rigorous experiments
  • deliver AI-driven solutions that scale to millions of Alexa customers