Applied AI​/ML & Causal Inference - Senior Associate

Job Summary

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.

We offer a competitive total rewards package including base salary determined by role, experience, skill set, and location. Eligible roles may receive commission‑based pay and/or discretionary incentive compensation. Benefits include comprehensive health care coverage, retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching, and more. Additional details will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal‑opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable…