Senior Consultant

Northern Trust

9 - 12 years

20 - 25 Lacs

bengaluru

Posted:2 weeks ago| Platform:

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Skills Required

logistic regression sas risk analytics analytical machine learning actuarial asset management risk management financial services monitoring

Work Mode

Work from Office

Job Type

Full Time

Job Description

Performs validation of Artificial Intelligence (AI), Machine Learning (ML) and Generative AI (GenAI) models
o Independently validate AI/ML and GenAI models across supervised, unsupervised, reinforcement learning and foundation model categories.
o Evaluate Gen AI model risk, including hallucination, prompt injection, data leakage, reproducibility, and alignment with Responsible AI principles.
o Assess model robustness, interpretability, fairness, and bias through quantitative and qualitative techniques.
Has solid understanding of risks that are posed by AI/ML models (Fairness, Privacy, Transparency and Explainability, etc.)
Has familiarity in financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, Factors.
Has fair understanding of stress testing, CCAR, CECL, etc.
Solves complex quantitative problems and takes a new perspective on existing solutions.
Acts independently and analyzes possible solutions using technical experience and judgment and precedents.
Develops and maintains an understanding of many algorithms across supervised learning, unsupervised learning and time series analysis.
Utilizes expertise in machine learning algorithms and statistics to challenge how algorithms are selected, trained and tested.
Also performs 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 ouputs 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 on-going basis to ensure models remain valid, as well as contribute in the bank-wide model risk and control assessment.
Support development of comprehensive documentation and testing of risk management framework. Deliver a work product that requires little revision.
Establish and maintain strong relationship with key functional stakeholders such as model developers, model owners, and users.

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