Applied AI/ML Modeling - Vice President
Salary not listed • New York, NY, United States • Full-time
Posted 1mo ago
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Job Location
New York, NY, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Our Consumer Bank AI Modeling team develops advanced analytics and machine learning solutions that inform high-impact decisions across field workforce effectiveness, customer engagement, and banker-led growth. As an Applied AI Modeling Vice President in this team, you will build and deploy advanced AI/ML models that measurably improve banker sales effectiveness and customer outcomes. Your models will help bankers deliver the right outreach at the right time to our customers, driving deposit growth, increasing customer retention, and strengthening relationships. You will operate in a highly governed environment and partner closely with product, UX, operations, and technology teams to translate modeling innovation into field-ready tools that bankers trust and adopt.
Responsibilities
- Develop and launch AI/ML models to solve complex business problems in Consumer Banking, focusing on sales effectiveness and banker enablement using techniques like deep learning, causal inference, contextual bandits, reinforcement learning, and constrained optimization.
- Lead modeling engagements end-to-end, including scoping use cases, defining success metrics, building project plans, and formulating testable hypotheses with large datasets.
- Translate model outputs into clear, actionable recommendations for non-technical partners and create narratives to drive adoption.
- Partner with governance, risk, and controls teams to expedite model reviews, document model intent and limitations, monitor performance, and maintain regulatory adherence.
Requirements
- Master's or Ph.D. in a quantitative discipline (e.g., Computer Science, Statistics, Machine Learning, Econometrics, Operations Research, Applied Mathematics).
- 5+ years of hands-on 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: recommendation/decisioning systems, ranking, constrained optimization, causal inference, online learning approaches, or behavioral modeling and human-in-the-loop systems.
- Demonstrated ability to communicate complex modeling concepts clearly to non-technical stakeholders and drive decisions.