Senior AI/ML Modeling for Banker Enablement

JP Morgan Chase

New York (NY)

On-site

USD 150,000 - 230,000

Full time

14 days+

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Job summary

JP Morgan Chase is seeking an Applied AI Modeling Senior Associate for the Consumer Bank AI Modeling team in New York. You will build and deploy advanced AI/ML models to improve banker sales effectiveness and customer outcomes, delivering right outreach at the right time and translating insights into field-ready tools for bankers to trust and adopt.

You will collaborate with product, UX, operations, and technology teams in a highly governed environment to drive adoption and impact, focusing on

Qualifications

Advanced degree (Master’s or Ph.D.) in a quantitative discipline such as Computer Science, Statistics, Machine Learning, Econometrics, Operations Research, Applied Mathematics, or a related field.
3+ years of hands-on, relevant industry experience developing and deploying AI/ML models in production, including statistical modeling and modern Machine Learning.
Proficient in Python with hands-on experience in ML/deep learning frameworks (TensorFlow, PyTorch) and core libraries (NumPy, Scikit-Learn, Pandas). Strong working knowledge of notebooks and cloud-based development/compute.
Deep expertise in at least one of the following, with meaningful exposure to at least one other: • Recommendation/decisioning systems (next-best-action/offer), ranking, and constrained optimization • Causal inference and uplift / treatment effect modeling for targeted interventions • Online learning approaches (contextual bandits, multi-armed bandits, reinforcement learning) • Behavioral modeling and human-in-the-loop systems that drive adoption and performance
Demonstrated ability to communicate complex modeling concepts clearly to non-technical stakeholders and drive decisions.

Responsibilities

Develop and launch AI/ML models that solve complex, ambiguous business problems in Consumer Banking, with emphasis on sales effectiveness and banker enablement (lead scoring, propensity modeling, next-best-action/next-best-offer, customer prioritization, retention, and cross-sell).
Participate in modeling engagements end-to-end, including scoping use cases with business partners, defining success metrics (incrementality, ROI, adoption), building project plans, and working with large datasets to formulate hypotheses.
Translate model outputs into clear, actionable recommendations for non-technical partners, and produce narratives that drive adoption (why this lead, why now, what action, expected outcome).
Partner with governance, risk, and controls teams to expedite model reviews, document model intent and limitations, monitor performance and drift, and maintain adherence to regulatory and model risk standards.