Senior Data Scientist , Alexa AI Aurora

Amazon Amazon · Big Tech · Bellevue, WA · Applied Science

Senior Data Scientist role focused on conversational AI, LLMs, NLP, and Generative AI for Alexa. The role involves defining strategy, leading initiatives from problem formulation to production, establishing evaluation frameworks, and driving consensus on agentic systems. It requires expertise in machine learning, generative AI, and computer vision, with a focus on delivering scalable and impactful solutions for millions of customers.

What you'd actually do

  1. Define the data science strategy for conversation modelling, content generation, and automated quality assurance by evaluating a wide range of methodologies across machine learning, generative AI, and computer vision, recommending the right approach based on business needs and scientific rigor.
  2. Lead the design and end-to-end delivery of complex, ambiguous data science initiatives from problem formulation through experimentation to production deployment, autonomously defining the problem space, selecting ideal solution approaches, and driving measurable business outcomes.
  3. Make high-judgment trade-offs across audio, text, and visual quality dimensions, balancing short-term customer needs against long-term platform extensibility, cost efficiency, and scalability while quantifying the impact of each decision.
  4. Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, identifying blind spots in existing measurements and proposing new mechanisms that institutionalize rigorous validation across customer touch points.
  5. Identify new business opportunities by staying at the forefront of AI/ML advances, translating emerging techniques into actionable data science directions with clear, quantifiable customer and business impact.

Skills

Required

  • 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
  • Define problem spaces and solution approaches
  • Advise senior leadership on data-driven decisions
  • Identify blind spots in existing metrics
  • Propose new measurements
  • Mentor and develop other data scientists
  • Setting standards for scientific rigor and operational excellence
  • Broad expertise across multiple data science disciplines
  • Deep understanding of how software systems, data pipelines, and business processes interact
  • Lead complex projects with minimal guidance
  • Make sound trade-offs between short-term customer needs and long-term technical investments
  • Deliver solutions that are scalable, reproducible, and actionable
  • Launch data science solutions that drive significant business outcomes
  • Strong communication skills
  • Ability to document and present technical findings to both technical and non-technical audiences
  • Commitment to collaborative teamwork
  • Define the data science strategy for conversation modelling, content generation, and automated quality assurance
  • Evaluate a wide range of methodologies across machine learning, generative AI, and computer vision
  • Recommend the right approach based on business needs and scientific rigor
  • Lead the design and end-to-end delivery of complex, ambiguous data science initiatives from problem formulation through experimentation to production deployment
  • Autonomously defining the problem space, selecting ideal solution approaches, and driving measurable business outcomes
  • Make high-judgment trade-offs across audio, text, and visual quality dimensions
  • Balance short-term customer needs against long-term platform extensibility, cost efficiency, and scalability
  • Quantify the impact of each decision
  • Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives
  • Identify blind spots in existing measurements
  • Propose new mechanisms that institutionalize rigorous validation across customer touch points
  • Identify new business opportunities by staying at the forefront of AI/ML advances
  • Translate emerging techniques into actionable data science directions with clear, quantifiable customer and business impact
  • Drive consensus across multiple teams on the architectural and methodological decisions underlying scalable agentic systems for conversation understanding and generation
  • Ensure alignment between data, software systems, and business processes
  • Set and continuously raise the bar for data science best practices across the team
  • Create models and analyses that are actionable, reproducible, and easy for others to contribute to and extend
  • Tackle the team's most complex technical problems
  • Apply broad expertise across multiple data science disciplines
  • Maintain practical focus on solution generalizability and customer value
  • Actively mentor and develop other data scientists in the organization
  • Lead scientific reviews
  • Provide constructive feedback on methodology and results
  • Keep the team current on data science advancements
  • Advance the team's scientific reputation through high-impact publications and presentations at top-tier venues
  • Generate intellectual property through patents

Nice to have

  • Experience managing data pipelines
  • Experience as a leader and mentor on a data science team

What the JD emphasized

  • state-of-the-art conversational AI
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • generative AI
  • computer vision
  • scalable agentic systems
  • proven track record of launching data science solutions that drive significant business outcomes is essential
  • Strong communication skills, the ability to document and present technical findings to both technical and non-technical audiences, and a commitment to collaborative teamwork are absolute requirements.

Other signals

  • Define the data science strategy for conversation modelling, content generation, and automated quality assurance by evaluating a wide range of methodologies across machine learning, generative AI, and computer vision
  • Lead the design and end-to-end delivery of complex, ambiguous data science initiatives from problem formulation through experimentation to production deployment
  • Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives
  • Drive consensus across multiple teams on the architectural and methodological decisions underlying scalable agentic systems for conversation understanding and generation