Applied AIML & 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

• Masters 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.