Investment Risk & Analytics - Quant Modeling Sr. Associate

Wealth Management (WM) Investment Risk & Analytics (IR&A) is responsible for the risk oversight of the Managed Strategies from end to end across the WM’s Private Bank and Consumer Bank businesses. Managed Strategies included JPMorgan’s own proprietary products and third-party funds to include registered funds, exchange traded notes (ETNs), exchange traded funds (ETFs), separately managed accounts, hedge funds, and private equity and real estate funds, etc.

We are looking for individuals who can partner with senior members of our team to oversee business activities, models and methodologies related to investment risk. with an emphasis on developing new tools and methodologies that will aid in risk quantification.

Job responsibilities:

• Design, develop, and deploy advanced AI/ML models to derive actionable insights, automate processes, and facilitate strategic decision-making across various business units.
• Collaborate with teams to design and oversee the end-to-end lifecycle of AI/ML initiatives – from problem scoping, data engineering, model development, validation, deployment, to monitoring.
• Lead the model development process, including tasks such as data wrangling/analysis, model training, testing, and selection.
• Conduct risk modeling, scenario analysis, and business impact evaluations to ensure AI/ML solutions are robust, ethical, and aligned with organizational goals.
• Drive experimentation with advanced techniques such as deep learning, generative AI (e.g., LLMs, GANs), and reinforcement learning to solve complex business challenges and create value.
• Utilize advanced statistics, econometrics and mathematical skills including graph theory applications, probability theory, stochastic calculus, Monte Carlo simulation, numerical analysis, optimization techniques and time series analysis.
• Collaborate with Technology on model testing, implementation, and production.
• Provide mentorship and oversight for junior data scientists to build a collaborative working culture.
• Deliver written, visual, and oral presentation of modeling results to business and technical stakeholders.
• Stay abreast of emerging trends and research in AI and machine learning, and assess their potential application within the organization.
• Contribute to continuous innovation and improvement in the efficiency and effectiveness of processes within the asset-class group, including discussions on overall team strategy.
• Represent risk analytics in governance forums, risk committees, and audit discussions.
• Work with risk managers and other stakeholders to address requests for additional analysis based on specific needs.
• Participate in regulatory and validation exams by providing documentation and responses to regulators and internal validators.
• Partner with non-credit risk groups to identify and understand the simultaneous impact of multiple risks, such as product, fiduciary, counterparty, concentration, ESG/climate, liquidity, and their effect on investments.

Required qualifications, capabilities, and skills:

• 5+ years of hands-on experience in data science, machine learning, or AI.
• Bachelors/Master/PhD degree in Computer Science / Data Science / Mathematics / Statistics / relevant STEM field is highly preferred.
• Demonstrated experience with generative AI technologies such as transformers, large language models, or diffusion models.
• Knowledge of key concepts in Statistics and Mathematics such as Statistical methods for Machine learning (e.g., ensemble methods, NLP, time-series), Probability Theory and Linear Algebra.
• Experience with Machine Learning & Deep Learning concepts including data representations, neural network architectures, custom loss functions.
• Foundational mathematical concepts, including continuity, differentiability, Taylor formulas, differential equations, integration, measure theory, linear algebra, discrete and continuous probabilities, Markov chains, regression.
• Have experience with investment products including fixed income, equity, and mutual funds.
• Programming skills in Python and knowledge of common numerical and machine-learning packages (like NumPy, scikit-learn, pandas, PyTorch, LangChain, LangGraph etc.).
• Experience with data visualization tools such as Tableau, Power BI, or similar.
• Logical thought process, ability to scope out an open-ended problem into data driven solution.
• Strong quantitative and analytical skills and ability to work with diverse cultures in a global team.