Senior Manager Data Science, Revenue & Growth

We're hiring a Senior Manager to lead two connected missions inside Checkr's Data & ML organization: (1) own the quantitative backbone of Checkr's revenue and financial data, and (2) accelerate growth, including self-serve SMB, product, pricing & packaging, etc., business through data science and experimentation.

This role owns the data science function that drives Checkr's growth across all levers: sales, product, planning, pricing, and packaging. That means forecasting and revenue models, cost and billing data products, unit economics, experimentation, activation and conversion analytics, and the quantitative inputs that shape how we package and price. These are the sources of truth our C-staff, Finance, and Product teams rely on to make decisions.

The mandate is broad on purpose. Growth at Checkr doesn't live in a single segment or motion; it spans self-serve SMB, enterprise expansion, and strategic finance. We believe there is significant untapped upside, and you will build and run the team that finds and captures it

This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Engineering, Product, RevOps, Marketing, Finance, and Executive teams. You'll also be a member of the Data & ML leadership team, which means you'll be directly involved in shaping Checkr's AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.

We are looking for someone based in San Francisco who has built and run data teams in fast-moving, impact-first environments. Less process, more shipping / less paperwork, more results.

What you’ll do

• Build and run the team. Hire, develop, and retain a high-performing group of data scientists and analysts across Revenue and Growth; set clear expectations, coach directly, and create an environment where strong ICs do their best work.
• Own the revenue data layer. Drive the roadmap for financial data science: revenue forecasting models, cost and billing data products, reconciliation pipelines, and Exec dashboards that our C-staff and board rely on for planning and strategy.
• Accelerate SMB growth. Lead the PLG data science push: experimentation, activation, and conversion analytics, package and pricing optimization, and propensity models that move self-serve revenue.
• Partner with Finance and Growth leadership. Be the go-to quantitative thought leader for the CFO org and Product leadership team; translate complex modeling work into clear recommendations that shape our roadmaps.
• Ship AI-powered workflows. Put AI to work on your team's processes: automate analytical pipelines, build agentic workflows for recurring analyses, and contribute reusable skills and context to Checkr's agentic platform. The expectation is that our teams operate AI-first.
• Raise the technical bar. Set and enforce standards for modeling rigor, data quality, pipeline reliability, and production-readiness. Your team's outputs inform board-level financial decisions; accuracy and trust are the baseline.

What you bring

• A Bachelor's or Master's degree in a quantitative field (Statistics, CS, Economics, Math, Engineering) or equivalent depth from experience
• 10+ years in data science, analytics, or quantitative roles, with clear progression in scope and impact
• 4-7+ years of direct people management, with demonstrated ability to hire strong talent and develop ICs into high performers
• Deep SQL and Python fluency; hands-on experience with modern ELT/data transformation tools (dbt, Snowflake, or equivalent); working knowledge of ML and forecasting methods
• Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• Track record of building and executing a multi-quarter roadmap for a data science function, with clear line-of-sight to business outcomes
• Strong executive communication skills; you can present complex quantitative work to a C-level audience and leave the room with a decision, not a follow-up
• Experience applying data science to revenue, growth, or financial problems in B2B SaaS or a high-growth startup environment

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