Senior Manager, Product Data Science

Snowflake Snowflake · Data AI · CA-Menlo Park, United States · Product Management

Senior Manager to lead a Data Science team focused on AI-native deliverables, including agentic services and AI evaluation for new product features. The role involves both team leadership and hands-on technical work, leveraging AI tools to accelerate product development and data-driven decision-making within the Product organization.

What you'd actually do

  1. Serve as the tech lead/manager of a Data Science team in the Product organization, leveraging AI tools and functions (e.g., Cortex AI functions, Agents, CoCo) to accelerate product development and data-driven decision-making.
  2. Mentor team members in core DS disciplines and on the effective and governed use of generative AI tools, including establishing AI best practices and guardrails.
  3. Drive the team's focus toward AI-native deliverables, such as building out Agentic services, semantic views, and conducting AI evaluation for new product features, strategically shifting away from automatable work.
  4. Be hands-on by serving as the primary data scientist on projects, specifically focusing on validating the accuracy and output quality of AI-powered analysis and developing POCs for new AI-driven product features.
  5. Partner with technical and business stakeholders to not only come up with solutions to stated problems, but encourage and enable the team to develop bottoms up ideas.

Skills

Required

  • Masters or PhD in Math/Statistics, Engineering, Computer Science, Science or related quantitative field
  • 10+ years experience as a Data Scientist
  • 5+ years of experience in building, managing, and leading a high-performing data science team
  • Experience in using data science to optimize the user experience.
  • Expert in SQL and Python.
  • Demonstrated experience with internal AI tools to build scalable data pipelines and drive analytical workflows.
  • Advanced knowledge/experience in machine learning and Large Language Models (LLMs), including the ability to critically evaluate and validate AI/ML outputs (e.g., using AI evaluation methods and understanding the limitations of AI Functions).
  • Ability to communicate and influence complex ideas to cross-functional stakeholders.

What the JD emphasized

  • AI-native thinkers
  • AI as a high-trust collaborator
  • AI-native deliverables
  • Agentic services
  • AI evaluation for new product features
  • AI-driven product features
  • Large Language Models (LLMs)
  • AI evaluation methods

Other signals

  • AI-native thinkers
  • AI as a high-trust collaborator
  • AI-native deliverables
  • Agentic services
  • AI evaluation for new product features
  • AI-driven product features
  • Large Language Models (LLMs)
  • AI evaluation methods