As Technical Lead, Data Science, your mission isn’t to ship the fastest model to production for its own sake — it’s to make sure every model, every metric, and every analytical decision coming out of the team is built on solid mathematical and statistical foundations. You’ll be the technical benchmark that raises the team’s rigor: challenging assumptions, questioning methodologies, and ensuring that inference — not just prediction — is done correctly.
This is a hands-on lead role, not a purely advisory one. You’re expected to review and mentor, yes — but just as often to open the notebook yourself, rework a flawed model specification, or step into a squad’s project directly when a methodological blocker needs to be resolved rather than explained. Guidance is the default mode; rolling up your sleeves is what happens when a deadline, a broken assumption, or a stuck teammate requires it.
Unlike a role centered on MLOps or deployment infrastructure, your territory is statistical design, model validity, and the quality of quantitative thinking. You’ll provide technical leadership — without necessarily having direct managerial reporting from the whole team — to data scientists who are embedded in specific projects and squads (Risk, Liquidity, Payments, among others), acting as their technical reference point, hands-on collaborator, and methodological quality auditor, while they remain aligned day-to-day with their squad’s priorities.