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

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Description

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 integrated into business decision flows. Translate analytics into business decisions: Communicate complex analytical findings clearly to Risk leadership and non-technical stakeholders and help turn model outputs into actionable business strategies. Contribute to Risk Analytics standards: Help build scalable methodologies for model development, validation, monitoring, documentation, and governance across Mexico, Brazil, and Colombia. Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. Preferred qualifications are a bonus, not a requirement. Must haves 4–6+ years of experience in Data Science, Risk Analytics, Credit Risk, or related analytical roles. At least 2 years of hands-on experience developing or validating credit risk models or other predictive risk models. Strong proficiency in Python and SQL for data manipulation, statistical analysis, and model development. Experience working with Databricks or similar cloud-based analytics platforms. Experience developing predictive models using libraries such as scikit-learn, LightGBM/XGBoost, PyTorch, or equivalent tools. Understanding of the full model lifecycle, including development, validation, backtesting, monitoring, recalibration, and documentation. Strong understanding of credit risk analytics, including concepts such as: delinquency and default; vintage analysis; roll rates; bad rates; portfolio performance; score discrimination and calibration; population and model stability. Experience working with large financial or transactional datasets and strong commitment to data quality and integrity. Ability to translate quantitative analysis into credit strategies and business recommendations. Working proficiency in English and Spanish. Academic background in Statistics, Mathematics, Economics, Engineering, Computer Science, Actuarial Science, Data Science, or a related quantitative field. Ability to work in a fast-moving environment and collaborate across Risk, Data, Engineering, and business teams. Nice to have Experience in fintech, lending, credit cards, payments, or B2B financial products. Experience with Latin American credit markets, particularly Mexico, Brazil, or Colombia. Knowledge of credit bureau data and alternative data sources. Experience with PD modeling, expected loss, ECL, LGD, or EAD methodologies. Experience with reject inference or modeling under selection bias. Experience defining credit line strategies, cutoffs, risk segmentation, or underwriting policies. Experience with MLflow, mo

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