Lead Data Scientist (Anonymized)

Led development of alternative credit scoring models utilizing mobile money, behavioral, and transactional data. Improved loan default prediction accuracy by 27% through advanced ensemble machine learning techniques and extensive feature engineering. Built automated machine learning pipelines leveraging Python, Airflow, and cloud infrastructure. Developed fraud detection and early-warning risk systems for digital lending platforms. Collaborated with cross-functional teams to deploy explainable AI models aligned with regulatory standards while mentoring junior team members.

Built application and behavioral scorecards for retail and SME lending portfolios. Conducted rigorous model validation, monitoring, and governance tasks using AUC, KS, PSI, and Gini metrics. Designed analytical dashboards for senior executives utilizing Tableau and Power BI. Optimized underwriting decision systems, resulting in accelerated customer approvals and reduced credit losses.

Analyzed large-scale telecom and mobile money datasets to generate customer analytics insights. Developed predictive churn and customer segmentation models using machine learning algorithms. Utilized big data tools including Hadoop and Spark to support scalable data analytics workflows.