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Clara is a leading B2B fintech for spend management in Latin America, serving over 20,000 businesses with corporate cards, bill pay, financing, and a powerful platform. The company is backed by top-tier investors including Kaszek, Monashees, Coatue, DST Global, and others.
You will join Clara's Risk Data Science team as a Senior Data Scientist focused on credit risk modeling. This role combines advanced analytics, machine learning, and credit risk expertise to develop and improve models and strategies supporting underwriting, portfolio management, and risk decision-making across Clara's markets in Mexico, Brazil, and Colombia.
Key responsibilities include:
- Design, build, validate, and maintain predictive models for credit origination, behavioral risk, portfolio management, and other risk use cases
- Own the full model lifecycle: problem definition, population and target construction, feature engineering, model development, validation, backtesting, calibration, monitoring, and recalibration
- Use SQL and Python to explore large datasets, identify portfolio trends, analyze delinquency and losses, and translate findings into actionable risk strategies
- Support development and optimization of underwriting strategies, score cutoffs, credit limits, segmentation, and portfolio management policies
- Build monitoring frameworks to track model discrimination, calibration, stability, data drift, portfolio trends, vintages, roll rates, delinquency, and key risk indicators
- Validate data sources, implement data quality controls, assess feature stability, and identify issues such as leakage, selection bias, or population drift
- Contribute to methodologies for addressing reject inference, selection bias, thin-file populations, and limited performance information
- Develop in a modern ML environment using Databricks, MLflow, GitHub, Python, SQL, and scikit-learn
- Collaborate with Data and Engineering teams to ensure reliable model deployment and integration into business decision flows
- Communicate complex analytical findings to Risk leadership and non-technical stakeholders
- Help build scalable methodologies for model development, validation, monitoring, documentation, and governance across Mexico, Brazil, and Colombia
You will work closely with Risk, Data, Engineering, Finance, and Operations teams in a fast-paced, high-ownership environment.
Requirements:
- 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 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 delinquency and default, vintage analysis, roll rates, bad rates, portfolio performance, score discrimination and calibration, and 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
Preferred qualifications:
- 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, model registries, version control, and reproducible ML workflows
- Experience with Git and GitHub
- Knowledge of data engineering concepts and ETL/data pipelines
- Experience taking models from development through implementation in partnership with Engineering
- Experience with visualization or BI tools such as Metabase
- Master's degree in Statistics, Data Science, Machine Learning, Economics, Finance, or a related quantitative field
