Senior Manager, Product Data Science
Snowflake
| Company | Snowflake |
| Category | Data & Analytics |
| Location | Menlo Park |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Manager |
| Salary | USD 227k–327k |
| Posted | 8 Jun 2026 |
| Last verified | 7 Aug 2026 |
| Source | Employer ATS (ashby) |
Description
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
We’re looking for a Senior Manager to lead our App Experience & Marketplace Data Science team in the Product organization.
You will lead a team covering app experience (UI, growth platform, analyst/admin/data engineer tools and experience) and data marketplace areas.
- Well designed high value apps are essential to make it simple for our customers to complete billions of SQL queries, deploy python, and use powerful AI tools every day
- Data marketplace makes it easy with a click of a button to get access to a wide range of 3rd party data instead of having to contact a sales representative at a data vendor and setup and maintain a complex API.
All the projects above allow the data scientist to work on very large datasets, and a variety of diverse interesting projects that span the domains of data engineering, analytics, statistics, and machine learning.
In this role, you will both lead a team, and be hands-on as a tech lead in this area. The manager will interact frequently with senior management, product managers, and engineering managers. The ability to effectively communicate complex technical ideas to a wide audience is crucial.
IN THIS ROLE YOU WILL:
- 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
QUALIFICATIONS:
- 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 comple