Title - ( Machine Learning Engineering (Quant)) Quantitative Contractor to support Model Risk Management for MACHINE LEARNING MODELS
Location - Remote 100%
RECRUITERS MUST RUN CHECKLIST KEY WORDS UNDERLINED
CLIENT FIRM, working with Banks and Hedge Funds, is lhigh-caliber quantitative contractors to support the Model Risk Management (MRM) team in validating highly complex machine learning models .
This role is specifically designed for technical experts capable of performing deep-dive, independent validations of models that power our most critical underwriting and credit decisions .
You will be responsible for the rigorous assessment of advanced algorithms including XGBoost and Transformers to ensure they are conceptually sound, mathematically robust, and safe for production use.
What You'll Do
Deep-Dive ML Validation: Execute rigorous, independent validations of complex machine learning models (e.g., Gradient Boosted Machines, Deep Learning, Transformers ) used for credit underwriting and risk management.
Technical Algorithm Challenge: Scrutinize mathematical logic, algorithm selection, and model architecture. Evaluate the appropriateness of hyperparameters and loss functions for specific credit use cases .
Model Estimation Review: Perform in-depth reviews of the model development process, including data partitioning strategies, feature engineering, and feature selection methodologies.
Advanced Outcome Analysis & Challenger Modeling: Independently design and build ML challenger models (e.g., using alternative architectures or features) to benchmark performance, evaluate model stability, and conduct rigorous sensitivity and backtesting analysis.
Engineering & Code Review: Conduct comprehensive, line-by-line reviews of production code. You must be able to navigate and work within complex engineering platforms to ensure that the technical implementation accurately reflects the intended model design and that the model integrates safely with the broader infrastructure . MUST BE ABLE TO READ PYTHON
Validation Reporting: Document detailed technical findings and recommendations for model owners, focusing on identifying critical weaknesses and opportunities for performance improvement.
What We Look For
Technical Experience: 5+ years of professional experience in a highly technical role such as Machine Learning Engineering , Model Development, or Quantitative Model Validation.
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