Head of Data, Tomo

• AI Fluent, including democratization and enablement of the whole Tomo team
• Strong data science and experimentation foundation: statistics and experimental design. You can independently design and analyze an experiment and defend the result
• Proficient in SQL and the modern data stack
• Range over depth: you've worn multiple hats and can operate credibly across data science, analytics/BI, and data engineering
• Strong business and stakeholder fluency — you translate ambiguous business questions into data work and communicate results to non-technical leaders
• A zero-to-one track record — you've been an early data hire, built a team or analytics function from scratch, or are a senior IC ready to step into your first leadership seat and own the build

Desirable

• Tomo is looking for a hands-on data leader to own data end-to-end and build out the function
• You'll inherit a mature data platform, a small, high-leverage team
• Your mandate will be to build that team out and push the ball forward
• This is a player-coach role: you'll be in the work yourself designing experiments, building data models, and partnering directly with the business while standing up the team and processes that let it scale
• You'll be the data face to the leadership team, translating questions from across the business - Marketing, Finance, Sales, Capital Markets, Mortgage Ops, Compliance, Product and Engineering - into measurement and models that move decisions
• Own the data function end-to-end — strategy, roadmap, prioritization across a broad stakeholder surface, and the health of the platform
• Build and develop the team — hire across data science, analytics/business intelligence, and data engineering, and grow the people already here
• Drive marketing measurement and experimentation — quantify the effectiveness of marketing spend and build attribution models
• Lead by building — write SQL, build dbt models, and work in notebooks alongside the team; set the technical bar by doing the work, not just reviewing it
• Keep the platform healthy — ensure pipelines and infrastructure are reliable, performant, and cost-efficient as the business scales
• Partner with Product and Engineering — shape how data is instrumented and captured so it's analyzable downstream, and help QA data at the source
• Be a trusted partner to leadership — own the data narrative with the exec team and make the call on what the team does and doesn't take on