Senior Credit Risk Data Scientist

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