Job Title:  Deputy Manager | Credit Risk Quant | Mumbai | Regulatory & Financial Risk

Job requisition ID ::  100775
Date:  Jun 4, 2026
Location:  Mumbai
Designation:  Deputy Manager
Entity:  Deloitte Touche Tohmatsu India LLP

Deputy Manager | Credit Risk Quant | Mumbai | Regulatory & Financial Risk
Job requisition ID : 100775 
Location: Mumbai
Entity: Deloitte Touche Tohmatsu India LLP 

The Team

Deloitte Strategy, Risk & Transaction helps entities mitigate risk while discovering new opportunities to create value. Our end-to-end risk services span all domains, from managing strategic risks in the C-Suite to improving board oversight, and from balancing financial and environmental policies to addressing cyber threats. Learn more about Risk, Regulatory & Forensic)

 

Your work profile

·      Perform independent validation of Advanced IRB / Foundation IRB models including:

·      PD, LGD, EAD, and CCF modelling methodologies

·      Rating system design and performance

·      RWA attribution and capital impact assessment

·      Assess model methodologies and assumptions for diverse wholesale product exposures including:

·      Corporate and SME lending (term loans, revolving credit, working capital facilities)

·      Project and infrastructure finance

·      Financial institutions & sovereign portfolios

·      Commercial Real Estate (CRE) and income-producing real estate

·      Trade finance, supply chain, and asset-based lending

·      Leveraged finance and private capital exposures

·      Evaluate model conceptual soundness, data representativeness, risk differentiation, and calibration methodology

·      Review and challenge:

·      Model segmentation, overrides, downturn calibration, and economic cycle considerations

·      Treatment of collateral, guarantees, credit mitigants, and default definitions

·      Regulatory compliance with Basel III/IV IRB requirements and regional supervisory rules

·      Conduct model performance testing including:

·      Discriminatory power, back-testing, stability monitoring, sensitivity, and benchmarking

·      Prepare high-quality validation documentation with clear findings, limitations, and remediation actions

·      Support regulatory engagements, addressing model findings, remediation evidence, and audit requests

·      Partner with Model Development, Credit Policy, Data Governance, and Capital Management teams to ensure models are fit-for-purpose and well-controlled

Key skills required: 

·      Experience with Corporate lending, project finance, commercial real estate, private equity exposures

·      Stress testing frameworks (CCAR/ICAAP) and IRB-to-IFRS 9 model linkages

·      Regulatory interactions with PRA, ECB, Fed/OCC, OSFI, etc.

·      Ability to articulate quantitative findings to non-technical senior stakeholders

·      Core Competencies

·      Effective challenge and independent risk oversight mindset

·      High attention to detail and documentation discipline

·      Stakeholder influencing and relationship management

·      Ability to manage multiple validations under tight timelines

·      Desired qualifications

·      Master’s degree or higher in Quantitative Finance, Statistics, Mathematics, Engineering, or related field

·      Experience in modelling or validation of Wholesale IRB capital models, IFRS9, Climate Risk Modelling experience within large banking organizations

·      Strong technical skills in Python, R, SAS, SQL and knowledge of credit modelling statistics

·      Deep knowledge of:

·      IRB rating system architecture and approvals

·      Basel III/IV capital rules for wholesale credit

·      Model risk governance expectations (e.g., SR 11-7)

·      Strong analytical judgment and written communication skills

·      Desired qualifications Master’s degree or higher in Quantitative Finance, Statistics, Mathematics, Engineering, or related field

·      4 to 6 years of experience in modelling or validation of Wholesale IRB capital models, IFRS9, Climate Risk Modelling experience within large banking organizations

·      Strong technical skills in Python, R, SAS, SQL and knowledge of credit modelling statistics