Senior Data Scientist – Risk Modeling (Hybrid)

About the role

Clara, a Latin American payment platform, is hiring a Senior Data Scientist for its Risk Data Science team. You will combine advanced analytics, machine learning and credit risk expertise to build and improve models that support underwriting, portfolio management and risk decisions across its markets. The role is hybrid, with time spent in the office, with customers and at home.

Key responsibilities

• Design, build, validate and maintain predictive models for credit origination, behavioral risk, portfolio management and other risk use cases.
• Own the full model lifecycle, from problem definition and feature engineering to validation, backtesting, calibration, monitoring and recalibration.
• Use SQL and Python to explore large datasets, analyze delinquency and losses and turn findings into risk strategies.
• Support underwriting strategies, score cutoffs, credit limits, segmentation and portfolio policies.
• Build monitoring frameworks for model discrimination, calibration, stability, data drift, vintages, roll rates and delinquency.
• Validate data sources, check data quality and assess feature stability, and contribute to methods for reject inference and thin-file populations.
• Work with Databricks, MLflow, GitHub, Python, SQL and scikit-learn to build reproducible solutions, and help deploy them with Data and Engineering teams.
• Communicate complex findings to Risk leadership and non-technical stakeholders, and help build scalable risk analytics standards across Mexico, Brazil and Colombia.

Required profile

• 4-6+ years in Data Science, Risk Analytics, Credit Risk or related roles, with at least 2 years building or validating credit risk models.
• Understanding of credit risk concepts such as delinquency, vintage analysis, roll rates, bad rates, score discrimination and calibration.
• Experience with large financial or transactional datasets and a commitment to data quality.
• Ability to translate quantitative analysis into credit strategies, and working proficiency in English and Spanish.
• Academic background in a quantitative field; a Master's degree is listed as a nice-to-have.

Required skills

• Python and SQL
• Databricks or similar cloud analytics platforms
• Predictive modeling with scikit-learn, LightGBM/XGBoost, PyTorch or equivalent
• Nice to have: PD/ECL/LGD modeling, reject inference, MLflow, Git/GitHub, ETL pipelines, Metabase

What we offer

• Competitive salary and stock options (ESOP) from day one
• An annual learning budget and accelerated development paths
• A high-talent-density team with English as the working language