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
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