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Cobre is Latin America's leading instant B2B payments platform, solving the region's most complex money movement challenges through advanced financial infrastructure. The company enables instant business payments—local or international, direct or via API—from a single platform, serving fintechs, PSPs, banks, and finance teams.
As Technical Lead, Data Science, you will serve as the technical benchmark for the data science organization, ensuring every model, metric, and analytical decision is built on solid mathematical and statistical foundations. This is a hands-on technical leadership role where you challenge assumptions, question methodologies, and ensure rigorous inference practices across the team.
You will provide distributed technical leadership to data scientists embedded in specific projects and squads (Risk, Liquidity, Payments, among others), acting as their technical reference point and methodological quality auditor. While they remain aligned day-to-day with their squad's priorities, you serve as their technical leader without necessarily having direct managerial reporting.
Key responsibilities include: defining and enforcing standards of statistical and mathematical rigor for experimental design, hypothesis testing, model selection, and validation; conducting critical peer reviews of statistical and ML models before implementation; designing and guiding advanced statistical methods (time series, causal inference, optimization, Bayesian models) applied to business problems such as risk, liquidity, fraud, and pricing; championing AI-assisted tools and workflows to accelerate the data science lifecycle; mentoring junior and senior data scientists on mathematical fundamentals; communicating complex analytical results to technical and non-technical stakeholders; and contributing to internal analytical quality frameworks.
You will work closely with Product, Risk, Engineering, and Compliance to ensure delivered models solve the right business problem with appropriate statistical confidence.
Required qualifications: Master's or Ph.D. in Statistics, Mathematics, Physics, Econometrics, Machine Learning, or related quantitative field; deep command of inferential statistics, probability, experimental design, and causal inference; strong Python coding skills; experience with data infrastructure tools like Snowflake; proficiency with standard ML libraries; and experience with deep learning frameworks (TensorFlow, PyTorch) for training and fine-tuning large language models.
