Quant Modeling Lead [Multiple Positions Available]

JPMorganChase•Jersey City, WA

10h•Onsite

About The Position

We are seeking a Quant Modeling Lead to join our team. This role involves conducting independent testing and developing benchmark models, mentoring junior team members, and evaluating risks associated with advanced machine learning and deep learning models. You will stay current with AI/ML advancements, guide model developers on risk governance, and draft/review model-related documents. Key responsibilities include coordinating governance activities, assessing model design and implementation against risk and regulatory requirements, and overseeing the end-to-end model risk lifecycle. You will also provide insights for model enhancements, present findings to senior management, represent the team in audits, and support team development initiatives like recruiting and hiring.

Requirements

Master's degree in Financial Engineering, Applied Mathematics, Operation Research, Statistics, Financial Mathematics, Computer Science, Data Science, or related field of study plus 2 years (24 months) of experience in the job offered or as Quant Modeling Lead or related occupation.
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, selection processes, tuning methodologies, and evaluation frameworks using XGBoost, Scikit-learn, PyTorch, TensorFlow, and Keras.
Benchmarking and analyzing data using algorithms including linear and logistic regression, clustering, and CART.
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 dataset computations with multithreading and multiprocessing in PySpark.
Managing data storage and processing with AWS.
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.