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.

   

Required Qualifications, Capabilities, and Skills

• Master's and 2+ years of hands on Machine Learning experience or fresh PhD grads in Computer Science, Statistics, Economics, Applied Math, Data Science, or a related quantitative field.

• 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.

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.

About UsAbout the TeamJ.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.​