Principal Data Scientist Lead

Microsoft Microsoft · Big Tech · Redmond, WA +4 · Technical Support Engineering

This Principal Data Scientist Lead role focuses on setting vision and standards for experimentation, causal inference, and machine learning within the Customer Experience and Success (CE&S) organization. The role involves driving complex analyses, architecting scalable frameworks, advising executives, and mentoring senior data scientists. A key aspect is deep experience in evaluating LLMs and AI agents, defining evaluation strategies, and establishing measurement standards for quality, safety, and reliability. The role is critical for ensuring methodological excellence and data storytelling, with a strong emphasis on causal inference and ML deployment at scale.

What you'd actually do

  1. Vision & Standards: You set the long-term vision and standards for experimentation, causal inference, and machine learning across the organization.
  2. High-Stakes Analysis: You drive the most complex, high-stakes analyses where correct causal identification is critical to strategic decisions.
  3. Frameworks: You architect scalable modeling and experimentation frameworks adopted across teams.
  4. Executive Advisory: You advise executives and cross-functional leaders on measurement strategy and interpretation of results.
  5. Mentorship: You mentor senior data scientists and shape hiring and technical growth for the discipline.

Skills

Required

  • Bachelor's Degree in Computer Science, Information Technology (IT), or related field AND 10+ years technical support, technical consulting experience, or information technology experience
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • Citizenship & Citizenship Verification: This position requires verification of U.S. citizenship due to citizenship-based legal restrictions.

Nice to have

  • Authority in causal inference — able to design novel identification strategies and adjudicate hard causal questions.
  • Deep statistical mastery — inference, uncertainty, experimental design, and modern statistical methods.
  • Expert ML skills — advanced modeling, validation, and production-grade deployment at scale.
  • Deep experience evaluating LLMs and/or AI agents — defining evaluation strategy, novel benchmarks, and measurement standards for model and agent quality, safety, and reliability.
  • Data storytelling — setting the bar for communicating insight through compelling data stories and enhanced visualizations in Python (e.g., matplotlib, seaborn, plotly) or other tools.
  • Demonstrated influence on company strategy through causal and statistical work.
  • Deep experience with causal ML tooling and large-scale experimentation platforms.
  • Expert proficiency in Python or R and SQL.

What the JD emphasized

  • Authority in causal inference
  • Deep statistical mastery
  • Expert ML skills
  • Deep experience evaluating LLMs and/or AI agents
  • Demonstrated influence on company strategy through causal and statistical work.

Other signals

  • setting the long-term vision and standards for experimentation, causal inference, and machine learning across the organization
  • driving the most complex, high-stakes analyses where correct causal identification is critical to strategic decisions
  • architecting scalable modeling and experimentation frameworks adopted across teams
  • advising executives and cross-functional leaders on measurement strategy and interpretation of results
  • mentoring senior data scientists and shaping hiring and technical growth for the discipline
  • championing methodological excellence, reproducibility, and compelling data storytelling
  • Deep experience evaluating LLMs and/or AI agents — defining evaluation strategy, novel benchmarks, and measurement standards for model and agent quality, safety, and reliability.