Applied AI/ML & Causal Inference - Senior Associate

As a Senior Applied AI/ML Associate within the Global Private Bank, you will own the full lifecycle of high-impact causal and predictive models serving clients across wealth management, deposit, 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 causal 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.
• Partner with the broader JPMorganChase AI/ML community, model risk, compliance, and peer LOBs to align on standards and amplify firm-wide impact.

• Master's or PhD in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.
• 3+ years of hands on Machine Learning 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 causal assumptions, limitations, and findings to non-technical stakeholders.

• 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.