Senior Consultant, Model Validation (Model Risk) at Northern Trust

Bengaluru, Karnataka, India

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Financial Services, BankingIndustries

Requirements

8 - 10 years of modeling or quantitative analysis experience, preferably in a disciplined financial or risk management environment
Advanced proficiency in financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, Factors
Solid understanding of risks posed by AI/ML models (Fairness, Privacy, Transparency, Explainability, etc.)
Good understanding of stress testing, CCAR, CECL, etc
Validates models typically developed in Python or R and occasionally SAS; able to challenge conceptual soundness of regression and machine learning models and assure appropriate and good quality data was used for development
Advanced proficiency in many algorithms across supervised learning, unsupervised learning, and time series analysis
Expertise in machine learning algorithms and statistics to challenge algorithm selection, training, and testing

Responsibilities

Act as a lead contributor in the discovery and diagnostic of model related risks including input data, assumptions, conceptual soundness, methodology, outcomes analysis, benchmarking, monitoring, and model implementation
Perform reviews of bank-wide quantitative models including models used for CECL and CCAR/DFAST stress testing, credit risk loss projections (PD, LGD, EAD), operational risk, interest rate risk models, AML (Anti-Money Laundering and Fraud Detection), and various machine learning models
Ensure model development, monitoring, and validation approaches meet regulatory expectations such as SR 11-7 and internal risk management needs
Evaluate conceptual soundness of model specifications; reasonableness of assumptions and reliability of inputs; completeness of testing performed to support the correctness of the implementation; robustness of numerical aspects; suitability and comprehensiveness of performance metrics and risk measures associated with model use
Review model documents and conduct test runs on model codes
Assess and measure the potential impact of model limitations, parameter estimation, error and/or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks
Document and present observations to Model Validation Team Lead and to model owners and users, recommend remediation action plans, track remediation progress, and evaluate remediation evidence
Monitor model performance reports on an ongoing basis to ensure models remain valid, as well as contribute to the bank-wide model risk and control assessment
Support development of comprehensive documentation and testing of risk management framework; deliver work product that requires little revision
Establish and maintain strong relationships with key functional stakeholders such as model developers, model owners, and users
Solve complex quantitative problems and take a new perspective on existing solutions; act independently and analyze possible solutions using technical experience and judgment and precedents

Skills

Key technologies and capabilities for this role

PythonRSASRegressionMachine LearningValue at RiskMonte CarloCAPMAI/MLStress TestingCCARCECL

Questions & Answers

Common questions about this position

What programming languages are used for the models in this role?

Models are typically developed in Python or R, and occasionally SAS.

What financial models does this role require proficiency in?

The role requires advanced proficiency in financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, and Factors.

What is the salary range for this Senior Consultant position?

This information is not specified in the job description.

Is this position remote or does it require office work?

This information is not specified in the job description.

What makes a strong candidate for this Senior Consultant role?

Strong candidates have advanced proficiency in Python/R financial models, expertise in challenging regression/ML models, solid understanding of AI/ML risks like Fairness and Explainability, and experience with regulatory frameworks like SR 11-7, CCAR, and CECL.