The mission:\n\nThe 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.\n\nThe expected outcome:\n\n* 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.\n* Deliver short-, mid-, and long-term forecasts for key portfolio and business indicators to guide strategic decision-making.\n* Continuously improve models and data pipelines based on new insights, feedback loops, and shifts in portfolio dynamics.\n* Build methodologies that reflect a clear understanding of the business and customer behavior—combining data science best practices with practical, real-world constraints.\n\nThe day to day tasks:\n\n* Model development & deployment: Design and implement predictive models (classification, regression, time series, etc.) to forecast credit risk metrics and optimize decision-making.\n* Model iteration & lifecycle management: Regularly retrain and improve models based on recent performance, business evolution, and new data availability.\n* 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.\n* Experimentation: Collaborate with cross-functional teams to design and evaluate A/B tests or quasi-experiments that inform modeling improvements.\n* Feature engineering: Create high-quality, interpretable features from raw transactional and behavioral data.\n* Data exploration & root-cause analysis: Use statistical techniques to detect anomalies, understand shifts in model performance, and identify risks or opportunities.\n* 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.\n* Documentation & reproducibility: Maintain clear documentation of models, assumptions, and decisions to ensure transparency, auditability, and future scaling.