The mission:

The Senior Credit Risk Data Scientist at Baubap is responsible for designing, implementing, and improving predictive models that directly impact our credit decisions and portfolio performance. This role will play a critical part in generating accurate forecasts, building data-driven methodologies, and continuously iterating based on real-world learning—always grounded in a deep understanding of the business context.

The expected outcome:

• Develop, deploy, and maintain machine learning models that improve the accuracy of forecasts across key business levers such as approval rate, disbursement rate, loss rate, and average loan amount.
• Deliver short-, mid-, and long-term forecasts for key portfolio and business indicators to guide strategic decision-making.
• Continuously improve models and data pipelines based on new insights, feedback loops, and shifts in portfolio dynamics.
• Build methodologies that reflect a clear understanding of the business and customer behavior—combining data science best practices with practical, real-world constraints.
  
  

The day to day tasks

• Model development & deployment: Design and implement predictive models (classification, regression, time series, etc.) to forecast credit risk metrics and optimize decision-making.
• Model iteration & lifecycle management: Regularly retrain and improve models based on recent performance, business evolution, and new data availability.
• Forecasting: Build robust models to predict portfolio KPIs over different time horizons (daily/weekly/monthly), including loss rate, disbursed amount, average ticket size, and approval rate.
• Experimentation: Collaborate with cross-functional teams to design and evaluate A/B tests or quasi-experiments that inform modeling improvements.
• Feature engineering: Create high-quality, interpretable features from raw transactional and behavioral data.
• Data exploration & root-cause analysis: Use statistical techniques to detect anomalies, understand shifts in model performance, and identify risks or opportunities.
• Business alignment: Partner closely with Risk, Product, Finance, and Data Engineering teams to ensure that models and methodologies are aligned with business goals and operational realities.
• Documentation & reproducibility: Maintain clear documentation of models, assumptions, and decisions to ensure transparency, auditability, and future scaling.
  

Why YOU should apply:

• 5+ years of experience developing, validating, and deploying predictive models in a production environment, preferably within financial services or credit risk.
• Strong proficiency in SQL for data extraction and transformation.
• Advanced skills in at least one programming language commonly used in data science, such as Python or R.
• Proven ability to build and tune machine learning models (e.g., classification, regression, time series forecasting), using libraries such as scikit-learn, XGBoost, LightGBM, etc.
• Experience maintaining and iterating on models based on real-world performance and shifting data patterns.
• Comfortable working with experimentation frameworks, A/B testing, and validation pipelines.
• Ability to translate complex technical insights into clear business recommendations.
• Strong understanding of statistical concepts and their application in risk modeling.
• Fluent in English (written and spoken); able to work and communicate effectively with an international and cross-functional team.
• Bonus: experience in financial risk areas
• Bonus: experience working with version control tools (e.g., Git), workflow managers (e.g., Airflow), and cloud-based data platforms (e.g., AWS, GCP, or similar).
  

What we can offer:

• Being part of a multicultural, highly driven team of professionals
• 20 vacation days / year + 75% holiday bonus (Prima Vacacional)
• 1 month (proportional) of Christmas bonus (Aguinaldo)
• Food vouchers
• Health & Life insurance
• Competitive salary