Data Scientist - Applied AI/ML

Join a world-class Applied AI/ML organization at JPMorganChase and help shape how teams across the firm use data science, machine learning, and Generative AI to solve real business problems.

As a Senior Associate in Applied Artificial Intelligence and Machine Learning within Shared Services, you design and deploy predictive models, advanced analytics, and large language model agentic solutions that orchestrate tools and workflows inside business processes. You wil build reusable services that teams can adopt to improve risk assessment support, legal and regulatory change mapping, control design and testing, sustainable monitoring, issue tracking, and governance reporting.

Job Responsibilities 

• Design, develop, and deploy predictive machine learning, advanced analytics, and generative AI solutions for complex business problems
• Build and integrate agentic workflows into end-to-end business processes, including retrieval-augmented generation, tool or function calling, routing, and structured outputs
• Prototype AI-enabled approaches quickly and harden successful prototypes into reusable, production-ready services with measurable outcomes
• Own end-to-end model delivery, including dataset preparation, feature engineering, training, validation, evaluation, deployment, and iteration
• Design, deploy, and operate production pipelines and services (batch and real-time), including monitoring, retraining strategies, and reliability and cost improvements
• Partner with product, engineering, and risk and controls stakeholders to define requirements, align on success metrics, and drive adoption
• Apply responsible AI and governance-aligned practices across the model and agent lifecycle, including evaluation, guardrails, and documentation
• Contribute reusable patterns, templates, and libraries that accelerate delivery across teams

Required qualifications, capabilities, and skills 

• Bachelor’s degree in data science, computer science, statistics, mathematics, or a related technical field (or equivalent practical experience)
• Three years of experience delivering end-to-end AI or machine learning solutions from prototype to production (or production-like) deployment
• Strong Python proficiency for data analysis, modeling, and production implementation
• Experience building, evaluating, and deploying predictive models (for example, classification, regression, or natural language processing) using common libraries (for example, PyTorch, TensorFlow, or scikit-learn)
• Experience building and deploying large language model workflows that include retrieval-augmented generation and tool or function calling
• Experience defining and using an evaluation approach for large language model solutions (for example, test sets, regression tests, or structured human review)
• Experience operating production pipelines or services, including versioning, continuous delivery practices, monitoring and alerting, and incident hygiene
• Working knowledge of cloud and or containerized environments (for example, Amazon Web Services, Microsoft Azure, Google Cloud Platform, or Kubernetes)
• Strong communication skills, including the ability to translate business problems into measurable technical outcomes and explain results to diverse audiences

Preferred qualifications, capabilities, and skills 

• Master’s degree or doctorate in a quantitative field
• Publications, patents, or meaningful open-source contributions related to machine learning or generative AI
• Experience scaling agentic systems across multiple use cases, including mature evaluation practices and quality dashboards
• Experience implementing guardrail patterns and operating controls for generative AI features in production
• Experience with large-scale data processing and cloud data services, and exposure to performance optimization for model serving
• Experience in financial services or other regulated industries, including comfort operating within governance and change management expectations
• Experience with specialized domains such as search and ranking, recommender systems, graph machine learning, or knowledge graphs