LatentView Analytics—Assistant Manager - Data Science

Job description

Description: Job Description: - Strong experience in Credit Risk Analytics, with a focus on Credit Card Portfolio Management and Risk Modeling. - Hands-on expertise in Application Scorecards and Behavioral Scorecards. - Strong experience in developing and working with PD (Probability of Default) and LGD (Loss Given Default) models. - Solid understanding of IFRS 9 / CECL frameworks, including Lifetime ECL and Staging. - Strong knowledge of Model Governance and associated documentation requirements. - Good understanding of the Credit Card Lifecycle and various stages of portfolio management. - Hands-on experience with Portfolio Interventions and credit risk strategies. - Strong knowledge of Credit Bureau Data and its application in risk modeling. - Experience working with Behavioral Signals and Transaction-Level Spend Data. - Proficiency in Python, SQL, SAS, Machine Learning, and Tableau. - Strong analytical, problem-solving, and data interpretation skills. - Excellent communication and documentation skills. Responsibilities: • Build statistically robust Application and Behavioral Scorecards, along with PD, LGD, and EAD models. • Design and develop loss forecasting frameworks using Vintage Analysis, Roll Rate/Markov Models, Survival/Hazard Models, and GLM/GBM techniques at both segment and portfolio levels. • Engineer and validate features from bureau data, internal behavioral data, transaction data, device signals, and alternative data sources with strong data quality controls. • Perform model calibration, backtesting, stability monitoring (PSI/CSI), discrimination analysis (KS/AUC), and calibration testing. • Implement model explainability techniques such as reason codes and SHAP, along with bias/fair lending checks and challenger/benchmark model development. • Align risk models with accounting and regulatory frameworks including IFRS 9/CECL and Basel PD/LGD/EAD concepts. • Develop credit card portfolio optimization models to drive customer spend, engagement, and portfolio growth. • Support portfolio spend growth initiatives through targeted segmentation, persona-based strategies, merchant targeting, and campaign recommendations to increase cross-border spend and customer engagement. Skills: ML, Data science, Risk Analytics Strong problem solving skills and communication