Data Scientist

Key Responsibilities:

Credit & Banking Model Development: Design, build, and recalibrate end-to-end quantitative models across the full credit lifecycle, including Application/Behavioral Scorecards, PD/LGD/EAD frameworks, IFRS 9/CECL loss forecasting, and stress testing.

Advanced Risk & Commercial Strategies: Develop specialized collections and commercial banking tools such as Propensity to Buy/Pay, Self-Cure, shadow ratings, and Early Warning Systems. Integrate key lifecycle models into decision engines for Risk-Based Pricing, dynamic Credit Line Management, and fraud detection scorecards.

Feature Engineering & Data Pipeline: Extract and process complex structured/unstructured datasets from credit bureaus, core banking systems, and alternative data sources. Perform Weight of Evidence (WOE) transformations, Information Value (IV) analysis, and feature selection.

Model Validation & Monitoring: Conduct rigorous Out-of-Time (OOT) and Out-of-Sample (OOS) validation. Track model drift, population stability index (PSI), characteristic stability index (CSI), Gini, KS statistics, and AUC-ROC to ensure ongoing accuracy and performance.

Regulatory Compliance & Governance: Prepare comprehensive model documentation in alignment with Model Risk Management frameworks and defend choices during internal validation and external audits.

Deployment & Strategy Integration: Partner with MLOps and Data Engineering teams to deploy models into real-time decision engines. Translate model outputs into actionable underwriting cut-offs, credit limit management strategies, and risk-based pricing.

Qualifications & Technical Stack

Education: Masters or Bachelors degree in Statistics, Mathematics, Computer Science, or a related quantitative field.

Experience: Minimum 4 years of hands-on experience developing statistical and machine learning models, specifically within banking, consumer lending, or fintech credit risk environments.

Domain Knowledge: Deep understanding of the end-to-end credit lifecycle (originations, account management, collections), delinquency tracking (Days Past Due - DPD), and credit bureau data structures.

Technical Requirements:

Programming: Advanced proficiency in Python and SQL. Experience with SAS or R is a plus.

Data & Cloud Platforms: Hands-on experience working with Big Data tools (PySpark, Databricks) and Cloud platforms (AWS / Azure / GCP).

Ready tools (PySpark, Databricks) and Cloud platforms (AWS / Azure / GCP).

Interested Candidates can send their resume over an Email: [email protected]