Data Scientist Applied AIML Senior Associate

Join a world-class Applied AI/ML organization at JPMorgan Chase and help shape how teams across the firm use data science machine learning and Generative AI to solve real business this shared services role youll support Consumer & Community Banking (CCB) Control Management Shared Services by delivering horizontal capabilities that strengthen how Control Managers operate day-to-day across Consumer & Community Banking businesses and functions (e.g. Auto Home Lending Credit Card Consumer Banking Business Banking Operations Branch Review and ICB) spanning core activities like ongoing risk monitoring process and regulatory understanding metric/breach review and building a holistic view of risks controls issues action plans applications and intelligent automation in the control environment.

As a Senior Associate in Applied AI/ML (Shared Services) you will design and deploy predictive ML advanced analytics andGenAI/LLM agentic solutionssystems that orchestrate tools workflows and large language models within business processesto create reusable services that scale across the Control Management lifecycle: maintaining risk assessment structures and tagging supporting legal/regulatory change and obligation mapping improving risk assessment and MRI alignment enabling control design/testing and sustainable monitoring accelerating issue identification/root-cause/action-plan tracking and validation and strengthening governance committees scorecards and reporting.

Job Responsibilities

• Design develop and deploy predictive ML advanced analytics GenAI/LLM and agentic AI solutions for complex business problems in shared services.
• Build and integrate agentic workflows (tool use RAG routing/planning structured outputs evals/guardrails) into end-to-end business processes to deliver context-aware insights and automation.
• Prototype AI-enabled approaches quickly then harden successful prototypes into reusable production-ready services with measurable outcomes.
• Own end-to-end model delivery: dataset manipulation/feature engineering training validation evaluation deployment and iteration.
• Design deploy and operate production ML pipelines and services (batch/real-time) including logging/metrics monitoring retraining/refresh strategies and reliability/cost/latency improvements.
• Partner with product engineering and risk/controls stakeholders to define requirements align on success metrics and drive adoption.
• Apply responsible AI governance and compliance-aligned practices throughout the model and agent lifecycle; share best practices and contribute reusable templates/libraries.

Required qualifications capabilities and skills

• Bachelors degree in data science computer science statistics mathematics or a related technical field (or equivalent practical experience).
• 5 years experience or demonstrated ability to set up and deploy AI/ML solutions end-to-end (prototype production or production-like) shown through prior roles internships research or substantial projects.
• Strong Python proficiency for data analysis modeling and production-grade implementation; solid dataset manipulation and feature engineering skills.
• Hands-on experience building evaluating and deploying predictive models and analytics solutions (e.g. classification/regression NLP) using common ML/deep learning libraries (e.g. PyTorch TensorFlow scikit-learn).
• Required agentic AI experience: built and deployed LLM-enabled agentic workflow (e.g. RAG tool/function calling routing/planning structured outputs) with an evaluation approach (test set regression tests human review or similar).
• Experience designing deploying and operating production ML/LLM pipelines or services including basic MLOps practices (versioning CI/CD for ML monitoring/alerting incident hygiene).
• Working knowledge of modern deployment environments: cloud (AWS/Azure/GCP) and/or containerized/distributed compute (e.g. Kubernetes).
• Strong communication and stakeholder partnership skills; ability to translate business problems into measurable technical outcomes and explain results to diverse audiences.

Preferred qualifications capabilities and skills

• Advanced education & thought leadership: Masters or PhD in a quantitative field; publications patents or meaningful open-source contributions in ML/GenAI.
• Advanced agentic/GenAI maturity: scaled agentic systems beyond a single use case; strong LLM evaluation discipline (golden sets automated regression quality dashboards) and guardrail patterns.
• Scale/performance & data ecosystems: GPU/inference optimization (e.g. Triton profiling) big data processing and cloud data services; exposure to RL or other advanced ML methods.

• Specialized ML domains & regulated environments: search/ranking recommenders graph ML/knowledge graphs; experience in financial services or other regulated industries and comfort operating within governance expectationsespecially for regulatory/change management workflows.

What Youll Build in Shared Services

• Reusable agent frameworks and patterns (routing tool-use workflow orchestration safety controls) that multiple teams can adopt.
• LLM-powered capabilities embedded in business processes (summarization classification decision support workflow automation) with measurable quality and risk controls.
• Deployed models supporting regulatory and change management (e.g. obligation/change classification and tagging QA/routing impact triage and audit-ready decision support) integrated into workflows with monitoring and governance.
• Evaluation and monitoring foundations (golden sets automated regression tests drift/quality dashboards) that standardize how AI is operated at scale.