Assistant Vice President/Vice President, Model Validation, Risk Management Group

DBS Bank · Mumbai

  • Experience10–11 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelexecutive
  • Posted22 Sept 2026

About DBS Bank

DBS Bank is hiring in Mumbai in financial services. This role looks for around 10+ years of experience.

Skills

  • credit risk modeling
  • model validation
  • statistical modeling techniques
  • regression analysis
  • econometrics
  • time series analysis
  • machine learning algorithms
  • Python
  • R
  • SAS
  • data analytics
  • model governance
  • SR 11-7
  • CECL
  • IFRS 9
  • Basel Accords
  • RBI guidelines
  • SQL

The role

A model validation leader at a banking and financial services company independently validates credit risk models across wholesale and retail banking, applying credit risk modeling and statistical modeling techniques to assess governance, performance, data quality, and regulatory compliance. The role uses Python and R to challenge model development and guide risk decisions.

Full job description

DBS Bank has been present in India for 30 years, opening its first office in Mumbai in 1994. DBS Bank India Limited is the first among the large foreign banks in India to start operating as a wholly owned, locally incorporated subsidiary of a leading global bank. As a trusted partner, DBS provides a range of banking services for large, medium, and small enterprises and individual consumers in India, focusing on a seamless customer experience that helps them ‘Live more, Bank less’. In November 2020, Lakshmi Vilas Bank was merged with DBS Bank India Limited. DBS Bank India is now present in ~350 locations in 19 Indian states.

Job PurposeWe are seeking a highly motivated and skilled individual to lead the Model Validation function with Risk Management Group. The successful candidate will be responsible for independently validating a diverse range of credit risk models used across both wholesale and retail banking businesses. This role involves assessing model design, data quality, implementation, and ongoing performance to ensure models are fit for purpose, compliant with regulatory requirements, and effectively manage financial risk.

Key AccountabilitiesIndependent Model Validation: Conduct end-to-end independent validation of credit risk models, including but not limited to: Wholesale Banking Models: PD, LGD, EAD models for corporate, commercial real estate, and other institutional exposures; stress testing models; counterparty credit risk models; credit rating models Retail Banking Models: PD, LGD, EAD models for mortgages, auto loans, credit cards, personal loans; application scorecards, behavioral scorecards, collections models. Methodology Review: Critically evaluate model development methodologies, underlying assumptions, conceptual soundness, and quantitative techniques. Data Quality Assessment: Assess the quality, completeness, and appropriateness of data used in model development and ongoing monitoring. Implementation Review: Verify the accurate and robust implementation of models within relevant systems.Performance Monitoring: Review and challenge ongoing model performance monitoring frameworks and results, including backtesting, benchmarking, and sensitivity analysis. Governance and Policy: Establish Governance processes and Model validation Policy in line with local regulatory and group requirements. Documentation & Reporting: Prepare comprehensive validation reports detailing findings, limitations, recommendations, and conclusions. Present findings to model owners, developers, and relevant committees.Regulatory Compliance: Ensure models and validation practices comply with internal policies and external regulatory requirements (e.g., SR 11-7, CECL, IFRS 9, Basel Accords). Challenging Model Development: Proactively engage with model developers to provide constructive challenge and guidance throughout the model lifecycle. Stakeholder Engagement: Collaborate effectively with model development teams, risk management functions, business lines, internal audit, and regulators. Continuous Improvement: Contribute to the enhancement of validation processes, methodologies, and tools.

Required Experience10+ years of experience in model validation or model development, specifically with exposure to credit risk models. Demonstrable experience with model review and validation pertaining to both wholesale banking and retail bankingUnderstanding of technical modelling requirements throughout model life cycle covering data analytics, coding, development, deployment, monitoring and maintenance for both traditional AI and Gen AI models Capacity to adopt new modelling areas (e.g., climate risk & Agentic AI) and problem solvingnderstanding of model governance requirements and principles

Education / Preferred QualificationsPost -graduate/ Graduate degree in quantitative discipline (such as Statistics, Mathematics, Quantitative Finance, Data Analytics or equivalent) is preferred

Core CompetenciesDeep understanding of credit risk concepts, including PD, LGD, EAD, ECL, credit ratings, credit scores, and stress testing.Familiarity with relevant RBI and Basel guidelinesUnderstanding of financial products and business processes in both wholesale and retail banking segments. Analytical & Critical Thinking: Exceptional analytical, problem-solving, and critical thinking skills with the ability to identify subtle model weaknesses and propose practical solutions. Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts clearly and concisely to both technical and non-technical audiences. Attention to Detail: Meticulous attention to detail and a commitment to producing high-quality work.Collaboration: Ability to work independently and as part of a team in a fast-paced environment.Experience in a regulated financial institution environment.

Technical CompetenciesStrong proficiency in statistical modeling techniques: regression analysis, econometrics, time series analysis, machine learning algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting).Expertise in programming languages for data analysis and statistical modeling: Python, R, SAS.Familiarity with database querying tools (SQL) is a plus.Experience with large datasets and data manipulation.

DBS India - Culture & BehaviorsPerformance through Value Based PropositionsEnsure customer focus by delighting customers & reduce complaintsBuild pride and passion to protect, maintain and enhance DBS’ image and reputationEnhance knowledge base, build skill sets & develop competenciesExecute at speed while maintaining error free operationsMaintain the highest standards of honesty and integrity