Credit Risk Modelers

Tata Consultancy Services; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tata Consultancy Services in Bengaluru, India, seeks an experienced Credit Risk Modeling & Advanced Analytics professional to lead the design, validation and governance of risk models across Retail and Commercial portfolios. The role emphasizes end-to-end model development, data science integration, and regulatory-aligned governance, with collaboration across IT and business units.

The candidate should bring expertise in PD/LGD/EAD, IFRS 9, stress testing, and AI/ML techniques within a heavily

Qualifications

• Proven expertise in building credit risk models and scorecards.
• Strong background in data exploration and feature engineering.
• Experience with regulatory frameworks and governance in risk modeling.

Responsibilities

• End-to-End Model Development & Design: lead research and build credit risk parameters and scorecards for Retail and Commercial lending portfolios.
• Advanced Analytics & Data: drive data exploration, feature engineering, and portfolio segmentation with AI/ML in a regulated setting.
• Implementation Support: work with IT and data teams to translate models into production systems and decision engines.
• Conceptual Soundness: conduct independent reviews of models, assessing theory, assumptions, and data lineage.
• Benchmarking & Replication: develop challenger models and verify accuracy and stability of main models.
• Stress Testing: assess sensitivity to macro scenarios and portfolio shifts.
• Performance Assessments: monitor model health with out-of-time analyses and calibration checks.
• Remediation Strategy: coordinate post-model adjustments or redevelopments with business teams.

Skills

Python

SAS

R

SQL

Tools

Tableau

Power BI

SQL

Job description

Role: Credit Risk Modeling & Advanced Analytics

Experience:5 to 15

Overview & Purpose

This document outlines the full spectrum of quantitative and governance roles within our global Credit Risk Modeling function. Depending on your expertise, you may specialize in a single core area (e.g., Development, Validation, Monitoring, or Governance) or operate across multiple stages of the model lifecycle. The purpose of this overview is to provide prospective an holistic view of our model landscape, the business lines we support, and the key responsibilities across the entire Model Risk Management (MRM) ecosystem

Scope of the Credit Risk Modeling Ecosystem

• Business Lines: Retail Banking (high-volume mass market, mortgages, credit cards, personal loans) and Commercial/Wholesale Banking (low-default portfolios, corporate lending, SME, institutional
• Model Types: Advanced IRB (PD, LGD, EAD), IFRS 9 / Expected Credit Loss (ECL), Origination & Behavioral Scorecards, Stress Testing, ALM, and emerging AI/Machine Learning models
• Regulatory Frameworks: APRA prudential standards, Basel III/IV accords, IFRS 9 accounting standards, US Federal Reserve SR 11-7 / SR 26-2, and UK PRA SS1/23

The Four Pillars of Credit Risk Modeling Responsibilities(Candidates may align with one or more of these pillars based on their background and career trajectory)

End-to-End Model Development & Design Methodology & Build: Lead the research, design, and development of core credit risk parameters and scorecards tailored to the distinct risk characteristics of Retail and Commercial lending portfolios

.Advanced Analytics & Data: Drive data exploration, feature engineering, and optimal portfolio segmentation. Evaluate and safely integrate emerging AI/ML techniques within a heavily regulated environment

• Implementation Support: Partner closely with IT, Data Engineering, and 1st Line Credit Officers to ensure models are accurately translated into production systems, decision engines, and business strategies (pricing, risk appetite).
• Conceptual Soundness: Conduct rigorous independent reviews of internally or externally developed models, thoroughly assessing underlying mathematical theory, assumptions, limitations, and data lineage
• Benchmarking & Replication: Develop quantitative challenger models and execute benchmark testing to independently verify the accuracy, stability, and predictive power of primary models
• Stress Testing: Assess model sensitivity to extreme macroeconomic scenarios and portfolio shifts, ensuring resilience under adverse global market conditions
• Performance Assessments: Execute periodic (monthly, quarterly, annual) out-of-time (OOT) monitoring cycles. Evaluate core health indicators including predictive power (discrimination), calibration accuracy (Expected vs. Actual outcomes), and population stability
• Threshold Breaches & RCA: Monitor outputs against approved RAG (Red/Amber/Green) tolerance thresholds. Lead investigations and Root Cause Analysis (RCA) for early warning signals or metric degradation
• Remediation Strategy: Partner with developers and the business to implement remediation actions, such as Post-Model Adjustments (PMAs), management overlays, off-cycle recalibrations, or triggering full redevelopments

Pillar 4: Model Risk Governance, Policy & Audit Traceability

• Enterprise Inventory & Lifecycle Control: Manage the enterprise model inventory, capturing accurate metadata and risk tiering. Strictly govern lifecycle activities from inception to retirement
• Issue & Exception Management: Centrally track open actions, validation findings, and governance milestones. Govern the review and escalation process for policy exceptions or threshold breaches
• Committee Reporting & Regulatory Response: Prepare governance packs, dashboards, and audit-ready artefacts for the Model Risk Committee (MRC). Act as a central coordinator for responses to Internal Audit and external regulators

Domain & Technical Expertise

• Quantitative Background: Extensive hands-on experience in one or more phases of the model lifecycle (Development, Validation, Monitoring, or Governance) within a global bank, financial institution, or specialist risk consultancy
• Statistical & Risk Theory: Deep understanding of predictive modeling, logistic/linear regression, survival analysis, transition matrices, and override behaviours
• Programming Languages: High proficiency in core statistical programming, specifically Python, SAS, or R, for building complex modeling pipelines or executing statistical analysis
• Data Architecture & Management: Strong SQL capabilities for extracting, reconciling, and manipulating massive credit datasets (default, recovery, origination) from complex databases and big data platforms
• Visualization & Governance Platforms: Familiarity with BI tools (Tableau, Power BI) for automated reporting, as well as enterprise Model Inventory workflow systems

Professional Attributes & Education

• Complex Problem Solving & Analytical Rigor: Exceptional attention to detail with the ability to translate ambiguous business or data anomalies into structured mathematical frameworks and actionable insights
• Stakeholder & Communication Skills: Proven ability to distill highly complex statistical concepts, model degradation, or governance requirements into clear insights for non-quantitative senior management, business leaders, and external regulators
• Process Discipline: Capable of maintaining strict version control, evidence traceability, and exceptional documentation standards (Model Development Documents, Validation Reports, Governance Charters) to withstand rigorous external scrutiny