Quantitative Modeling Lead [Multiple Positions Available]

Duties: Conduct independent tests and develop benchmark models that serve as reference points for evaluating model performance.

Evaluate the risks associated with machine learning and deep learning models by systematically identifying potential vulnerabilities and recommending mitigation strategies to minimize model risk.

Draft and review documentation related to model reviews.

Mentor and train junior team members, sharing best practices and supporting their professional development.

Stay current with advancements in AI/ML algorithms and fraud detection techniques, incorporating new insights and technologies into the model validation process.

Guide model developers and business partners through model risk governance requirements and processes.

Coordinate and lead governance activities for the team such as performance monitoring and the annual status assessment, to ensure ongoing model integrity and compliance.

Assess and challenge model design and implementation to ensure alignment with risk management and regulatory requirements.

Oversee the end-to-end model risk lifecycle, including validation, performance monitoring, change management, and issue remediation.

Provide expert insights and recommendations for model enhancements, and present findings to senior management and key stakeholders.

Represent the team in audit and regulatory reviews, ensuring timely and accurate responses to inquiries.

Support team development initiatives, including recruiting, hiring, and onboarding new team members.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Computational Science and Engineering, Quantitative & Computational Finance, Data Science, Machine Learning, Statistics, Mathematics, Computer Science, Computer Engineering or related field of study plus 2 years (24 months) of experience in the job offered or as Quantitative Modeling Lead, Quantitative Analytics Associate, Quantitative Analyst, or related occupation.

Skills Required: This position requires two (2) years of experience with the following: performing model risk assessments of XGBoost models, including validation of statistical methods, hyper-parameters, and interpretability using Shap; conducting independent reviews of ML pipelines, validating feature engineering and selection processes, tuning methodologies, and evaluation frameworks using XGBoost, Scikit- learn, PyTorch, TensorFlow, and Keras; using algorithms, including linear and logistic regression, clustering, and CART to benchmark and analyze data; evaluating models using metrics including AUC-ROC, Precision-Recall, cross-entropy loss, and KS; developing models using Python, R, and SQL with object-oriented programming principles; manipulating data, including imputation, encoding, and normalization, using NumPy, Pandas, PySpark, and Dask; visualizing results with Matplotlib, Seaborn, and ggplot; optimizing large dataset computations with multithreading and multiprocessing in PySpark; managing data storage ...