Credit Risk Modelling and Portfolio Optimization

Build statistically robust application and behavior scorecards; develop PD, LGD, and EAD models
Design loss‑forecasting frameworks (vintage, roll‑rate/Markov, survival/hazard, GLM/GBM) at segment and portfolio levels.
Engineer features from bureau, internal behavioral, transaction, device, and alternative data with rigorous data QC.
Calibrate and back test models; perform stability monitoring (PSI/CSI), discrimination (KS/AUC), and calibration tests.
Implement explain ability (reason codes/SHAP), bias/fair‑lending checks, and challenger/benchmark models.
Align models to accounting and capital frameworks (IFRS 9 or CECL; Basel PD/LGD/EAD concepts).
Development of credit card portfolio optimization data models to drive spends and engagement
Portfolio Spends Growth – Targeting basis segments/persona, merchants, campaign recommendation to drive cross border

SKILLS & EXPERIENCE

Qualifications:

For example,
Masters in Statistics, Economics from a reputed organization

Experience:

8+ Years

Must have

Skills:

Must have working knowledge of credit cards
Must have hands on knowledge of building application score, behavior score, PD, LGD models
Solid understanding of IFRS 9 or CECL (lifetime ECL, staging/pooling), and model risk governance
Solid understanding of credit card lifecycle and credit card portfolio interventions
Python, ML, SQL, SAS and Tableau
Communication skills to explain complex methods to non‑technical audiences; clear documentation.
Exposure to bureau data (e.g., attributes, reason codes), alternative data, and identity/behavioral signals.
Exposure to transaction level credit card spends data