Senior Data Scientist – Risk Modeling

Clara

. Develop, validate, and maintain predictive credit risk models for credit origination, behavioral risk, portfolio management, and other risk use cases .

Posted 10/1/2026full-timeBogota • ColombiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and validating predictive credit risk models, utilizing Python and SQL for data analysis and model development. Proficient in translating quantitative findings into actionable credit strategies while ensuring data quality and integrity.

Highest-signal resume keywords

Credit Risk Model DevelopmentPython ProficiencySQL ProficiencyDatabricks ExperiencePredictive Modeling with Scikit-Learn

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingFeature EngineeringStatistical AnalysisModel ValidationBacktestingData Quality ControlPopulation Drift AssessmentDelinquency AnalysisPortfolio ManagementScore Calibration

Soft Skills

CollaborationCommunicationProblem-SolvingAdaptability

Tools & Technologies

DatabricksMLflowGitHubScikit-LearnLightGBMXGBoostPyTorch

Industry Keywords

Credit RiskRisk AnalyticsFinancial DatasetsLatin American Credit MarketsReject InferenceB2B Financial ProductsCredit Bureau Data

Tech Stack

Tools & technologies

CloudPythonPyTorchScikit-LearnSQL

About the role

Key responsibilities & impact

Develop, validate, and maintain predictive credit risk models for credit origination, behavioral risk, portfolio management, and other risk use cases
Own the full modeling lifecycle, including problem definition, population and target construction, feature engineering, development, validation, backtesting, calibration, monitoring, and recalibration
Use SQL and Python to explore large datasets, identify portfolio trends, analyze delinquency and losses, and develop actionable risk strategies
Support underwriting strategies, score cutoffs, credit limits, segmentation, and portfolio management policies
Build monitoring frameworks for model discrimination, calibration, stability, data drift, portfolio trends, vintages, roll rates, delinquency, and other risk indicators
Validate data sources, implement data quality controls, assess feature stability, and identify leakage, selection bias, and population drift
Contribute to methodologies addressing reject inference, selection bias, thin-file populations, and limited performance information
Use Databricks, MLflow, GitHub, Python, SQL, scikit-learn, and other modeling tools to build reproducible, documented analytical solutions
Collaborate with Data and Engineering teams to deploy and integrate models into business decision flows
Communicate analytical findings to Risk leadership and non-technical stakeholders and translate model outputs into business strategies
Build scalable risk analytics methodologies across Mexico, Brazil, and Colombia

Requirements

What you’ll need

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 are a bonus, not a requirement; nice-to-have experience includes fintech, lending, credit cards, payments, B2B financial products, Latin American credit markets, credit bureau and alternative data, PD/expected loss/ECL/LGD/EAD methodologies, reject inference, credit line strategies, MLflow, Git/GitHub, data engineering, model implementation, and Metabase

Benefits

Comp & perks

Competitive salary and stock options (ESOP) from day one
Annual learning budget and internal accelerated development paths
Flexible vacation
Hybrid work model focused on results
Flexible working arrangement with time split between office, customer visits, and home
Multicultural team with daily exposure to Portuguese, Spanish, and English
High-ownership environment
Smart, ambitious teammates
Open, inclusive, and values-driven environment