Lead and hands-on manage a Product Data Science team focused on app experience, marketplace, and AI-native product features. Drive AI evaluation, agentic services, and mentor team on ML/LLM best practices.
227k – 327k/yr
Hybrid10+ YOEData Science
About the role
Responsibilities
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.
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.
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.
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.
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.
Maximize the impact of the team, by making sure the data scientists have the best and correct context, and ensuring their skill sets are properly matched to the project.
Grow the team, when appropriate. Convince job candidates on why Snowflake, and the product data science team is an awesome place to work.
Requirements
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
Skills
SQLPythonMachine LearningLLMsAi EvaluationData PipelinesAnalyticsStatisticsGenerative AICortex Ai
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