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  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.

• 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 JPMorgan
  
  Chase 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.