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Applied Artificial Intelligence/ Machine Learning Lead - Vice President
senior · Artificial Intelligence
As an Applied AI/ML Vice President within Global Private Bank, you'll own the full lifecycle of high-impact casual and predictive models serving clients across wealth management, lending, and advisory, from problem framing with business stakeholders through production deployment at scale. You will tackle some of the most data-rich, complex client problems in financial services, where rigorous casual reasoning not just predictive accuracy. Drives the decisions that matter.
Job Responsibilities:
- Frame ambiguous client and operational questions as causal problems — distinguishing prediction from intervention, identifying confounders, and designing the right estimand with Private Bank business leads.
- Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust estimation), experimentation, and classical/generative ML where appropriate.
- Own model quality, identification assumptions, sensitivity analysis, evaluation frameworks, monitoring, and post-deployment iteration.
- Drive productionization and MLOps practices in collaboration with engineering across distributed data infrastructure.
- Track applied research in causal ML, double machine learning, and agentic/LLM systems; translate promising work into production-ready solutions.
- Mentor junior data scientists and set technical standards for causal rigor across the team.
- Partner with the broader JPMorganChase AI/ML community, model risk, compliance, and peer LOBs to align on standards and amplify firm-wide impact.
Required qualifications, capabilities, and skills:
- Master's or PhD in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.
- At least 5 years of hands-on ML experience in production environments, with a substantial portion focused on causal inference.
- Deep expertise in causal inference methods: potential outcomes framework, propensity score methods, instrumental variables, difference-in-differences, regression discontinuity, synthetic controls, doubly robust and double/debiased ML estimators, and uplift / heterogeneous treatment effect modeling.
- Demonstrated experience designing and analyzing experiments (A/B tests, switchback, quasi-experiments) and reasoning carefully from observational data when experimentation is infeasible.
- Hands-on experience with LLMs and agentic AI — fine-tuning, RAG pipelines, prompt engineering, and the design and deployment of multi-step / tool-using agents in production.
- Strong Python skills; proficiency with causal libraries (DoWhy, EconML, CausalML) alongside PyTorch, scikit-learn, and modern LLM/agent frameworks.
- Experience with large-scale data processing: Spark, Hive, SQL.
Proven ability to communicate casual assumptions, limitations, findings to non-technical stakeholders.
Preferred qualifications, capabilities, and skills:
- Financial services experience — wealth management, lending, or advisory.
- Bayesian and hierarchical modeling; structural causal models; sequential decision-making / contextual bandits.
- Experience applying causal reasoning to LLM and agent evaluation — counterfactual eval, off-policy estimation, or treatment-effect framing of agent interventions.