Quant Modeling Lead [Multiple Positions Available]

JPMorgan Chase & Co.•Jersey City, NJ

16h•$164,000 - $215,000•Onsite

About The Position

The Quant Modeling Lead will be responsible for conducting independent testing and developing benchmark models to serve as reference points for model performance. This role involves mentoring and training junior team members, evaluating risks associated with advanced machine learning and deep learning models, and staying current with advancements in AI/ML algorithms and fraud detection techniques. The lead will guide model developers and business partners through model risk governance requirements, draft and review model review documents, and coordinate governance activities for the team. Additionally, the role requires assessing and challenging model design and implementation, overseeing the end-to-end model risk lifecycle, providing insights and recommendations for model enhancements, and representing the team in audit and regulatory reviews. Support for team development initiatives, including recruiting, hiring, and onboarding, is also a key aspect of this position.

Requirements

Master's degree in Financial Engineering, Applied Mathematics, Operation Research, Statistics, Financial Mathematics, Computer Science, Data Science, or related field of study.
2 years (24 months) of experience in the job offered or as Quant Modeling Lead or related occupation.
Two (2) years of experience with performing model risk assessments of XGBoost models, including validation of statistical methods, hyper-parameters, and interpretability using Shap.
Two (2) years of experience conducting independent reviews of ML pipelines, validating feature engineering, selection processes, tuning methodologies, and evaluation frameworks using XGBoost, Scikit-learn, PyTorch, TensorFlow, and Keras.
Two (2) years of experience benchmarking and analyzing data using algorithms including linear and logistic regression, clustering, and CART.
Two (2) years of experience evaluating models using metrics including AUC-ROC, Precision-Recall, cross-entropy loss, and KS.
Two (2) years of experience developing models using Python, R, and SQL with object-oriented programming principles.
Two (2) years of experience manipulating data including imputation, encoding, and normalization using NumPy, Pandas, PySpark, and Dask.
Two (2) years of experience visualizing results with Matplotlib, Seaborn, and ggplot.
Two (2) years of experience optimizing dataset computations with multithreading and multiprocessing in PySpark.
Two (2) years of experience managing data storage and processing with AWS.
Two (2) years of experience conducting analysis in graphical database using TigerGraph.

Responsibilities

Conduct independent testing and develop benchmark models to serve as reference points for model performance.
Mentor and train junior team members, sharing best practices and supporting their professional development.
Evaluate risk associated with advanced machine learning and deep learning models by identifying potential vulnerabilities and recommending mitigation strategies to minimize model risk.
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.
Draft and review documents related to model review.
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 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.