Head of Data

About the role

We're hiring a Head of Data to turn AngelList's data into an unfair advantage.

The business is unusual: a marketplace, an investing platform, a banking partner, and a software + services business, all reinforcing each other. The data implications are extraordinary, and most of the interesting questions are still unanswered.

Responsibilities

• Decision science. Own attribution, incrementality, LTV / CAC, customer segmentation, forecasting, and experimentation across our business units. Set the methodologies, defend them, and improve them as we learn.
• Flywheel measurement. Build the analytical view of how our businesses (admin, Carry, Ark, Meridian) reinforce each other, where the flywheel is real, where it’s aspirational, and what investments accelerate it.
• Experimentation. Make running good experiments the default for product, marketing, and growth, with the infrastructure and review process to back it up.
• The data platform. Inherit a working platform with real gaps. Decide which gaps to fill, which to accept, and how to keep investing so the foundation grows with the demands you’re putting on it.
• The team. Three people today, a mix of data engineering and analytics. Grow it deliberately. We expect early hires to be data scientists who reflect the bar you set, not headcount for its own sake.
• Executive partnership. Be a thought partner to the CFO, CEO, and the GMs, in the room for capital allocation and GTM decisions, not summarizing them afterward.

What We’re Looking For

• 8+ years in data science, decision science, or growth analytics, including hands-on practitioner work. At least 2 years leading teams.
• Deep expertise in some combination of attribution, causal inference, experimentation, LTV / CAC, segmentation, forecasting, and marketplace measurement. Demonstrated, not aspirational.
• Strong Python and SQL. You’ve built models recently enough to still be opinionated about how to build them.
• Track record of changing executive decisions through analysis. You can point to specific calls you helped get right, and ones you got wrong, and what you learned.
• Exceptional written communication. We expect to read what you write and act on it.
• Maturity with the platform layer. You don’t have to be the deepest data engineer in the room, but you have to make good architectural calls and respect the work of the engineers on your team.
• A genuine appetite for being a player-coach. If your career goal is to never write SQL again, this is the wrong role.
• Marketplace, fintech, or financial services background is a bonus, not a requirement.