Data Science

What is Cobre, and what do we do?

Cobre is Latin America’s leading instant b2b payments platform. We solve the region’s most complex money movement challenges by building advanced financial infrastructure that enables companies to move money faster, safer, and more efficiently.

We enable instant business payments—local or international, direct or via API—all from a single platform.

Built for fintechs, PSPs, banks, and finance teams that demand speed, control, and efficiency. From real-time payments to automated treasury, we turn complex financial processes into simple experiences.

Cobre is the first platform in Colombia to enable companies to pay both banked and unbanked beneficiaries within the same payment cycle and through a single interface.

We are building the enterprise payments infrastructure of Latin America!

What we are looking for:

As a Senior 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.

What would you be doing:

• Methodological rigor: Define and enforce standards of statistical and mathematical rigor for all of the team's work — experimental design, hypothesis testing, model selection, validation, and treatment of bias and uncertainty.
• Distributed technical leadership: Act as the technical (not hierarchical) leader of data scientists embedded in different teams/projects — reviewing their work, and directly stepping in to fix, rebuild, or co-build a model or analysis yourself when a methodological blocker needs hands-on resolution rather than a review comment.