Senior Data Scientist — Risk Modeling

Clara is the leading spend management platform in Latin America. More than 40,000 businesses run on our corporate cards, bill pay, financing and B2B payments, and in 2025 the Financial Times named Clara the fastest-growing company in Latin America. We're ~400 people, backed by Kaszek, monashees, Coatue, DST Global, ICONIQ, General Catalyst and Goldman Sachs, among others.

• 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.

• 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.

• 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.

Why Clara

• A high talent-density team driven to make an impact in Latin America — low ego, high ownership.
• Competitive salary and stock options (ESOP) from day one.
• An annual learning budget and accelerated development paths.
• English is our working language, with Spanish and Portuguese every day.

How we work

• Clarity — Open and direct communication: we say what we mean and give context where it matters.
• Simplicity — If it can be simpler, it should be.
• Ownership — We own everything we do, end to end. When something needs to happen, we don't wait.
• Pride — We hold ourselves to a high standard, and it shows in what we deliver.
• ABC (Always Be Changing) — We adapt fast, embrace change, and keep learning.
• Inclusivity — Diverse ideas, stories and perspectives build a better Clara.

Hybrid at Clara

Claridians split their time between the office, customers and home. We don't set a minimum number of office days for most roles, but we expect you to spend time there naturally, and most days during your ramp-up or when your leader asks.

If you meet the must-haves, apply — the nice-to-haves are a bonus, not a requirement.