Senior Data Scientist – Risk Modeling (Senior Data Scientist – Modelado de Riesgos) - Hybrid

Clara·Bogota D.C. / DC / Colombia; Mexico City / CDMX / Mexico; Sao Paulo / SP / Brazilsenior

In short

→Modelar riesgos crediticios con ML para decisiones de underwriting y gestión de cartera.
→Trabajar en todo el ciclo de vida del modelo: desde diseño hasta monitoreo y recalibración.
→Destacado: equipo multidisciplinario con impacto directo en empresas masivas en LATAM.

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The questions they'll ask you

1. ¿Cómo manejas el drift de datos en un modelo de riesgo en producción?

🔒 +7 more questions

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💵 USD · Remote · No visa

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What they ask for

✓Experiencia sólida en modelado de riesgos crediticios (origination, comportamiento, cartera).
✓Dominio avanzado de Python y SQL para análisis de grandes conjuntos de datos.
✓Experiencia con el ciclo completo de desarrollo de modelos: construcción, validación, monitoreo.
✓Conocimiento práctico de métricas de riesgo: discriminación, calibración, estabilidad, drift.
✓Capacidad de trabajar en equipos multidisciplinarios (Riesgo, Datos, Ingeniería, Operaciones).
✓Experiencia en Databricks, MLflow, GitHub y entornos de ML reproducibles.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonSQLDatabricksMLflowGitHubscikit-learnPandasNumPyPostgreSQLAirflow

Who should you write to at Clara?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

Ready to accelerate your career? Clara is the fastest-growing company in Latin America. We've built the leading solution for companies to make and manage all their payments. We already help over 20,000 large and growing businesses operate with agility and financial clarity through locally issued corporate cards, bill pay, financing, and a powerful B2B platform built for scale. Clara is backed by some of the most successful investors in the world, including top regional VCs like monashees, Kaszek, and Canary, and leading global funds like Notable Capital, Coatue, DST Global Partners, ICONIQ Growth, General Catalyst, Citi Ventures, SV Angel, Citius, Endeavor Catalyst, and Goldman Sachs - in addition to dozens of angel investors and local family offices. We’re building the financial infrastructure that powers high-performing organizations across the region. We invite you to join us if you want to be part of a fast-paced environment that will accelerate your career and support you to do some of the best work of your life alongside a passionate and committed team distributed across the Americas. What you'll do We're looking for a Senior Data Scientist – Risk Modeling to join Clara’s Risk Data Science team. In this role, you will combine advanced analytics, machine learning, and credit risk expertise to develop and improve models and strategies that support underwriting, portfolio management, and risk decision-making across Clara’s markets. You will work closely with Risk, Data, Engineering, Finance, and Operations , taking analytical problems from exploration and model development through validation, monitoring, and business implementation. Your responsibilities will include: • Develop credit risk models: Design, build, validate, and maintain predictive models for credit origination, behavioral risk, portfolio management, and other risk use cases . • Own the modeling lifecycle: Work across the full model lifecycle, including problem definition, population and target construction, feature engineering, model development, validation, backtesting, calibration, monitoring, and recalibration. • Drive advanced risk analytics: Use SQL and Python to explore large datasets, identify portfolio trends, analyze delinquency and losses, and translate findings into actionable risk strategies. • Strengthen credit decisioning: Support the development and optimization of underwriting strategies, score cutoffs, credit limits, segmentation, and portfolio management policies. • Monitor model and portfolio performance: Build monitoring frameworks to track model discrimination, calibration, stability, data drift, portfolio trends, vintages, roll rates, delinquency, and other key risk indicators. • Improve data and modeling quality: Validate data sources, implement data quality controls, assess feature stability, and identify potential issues such as leakage, selection bias, or population drift. • Work with rejected and unobserved populations: Contribute to methodologies for addressing reject inference, selection bias, thin-file populations, and limited performance information where relevant. • Develop in a modern ML environment: Use Databricks, MLflow, GitHub, Python, SQL, scikit-learn , and other appropriate modeling tools to build reproducible and well-documented analytical solutions. • Support model implementation: Collaborate with Data and Engineering teams to ensure models developed by Risk Data Science can be reliably deployed and

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