Data Scientist Associate

Shape the future of intelligent products by building production-ready generative AI services that create measurable impact. Join a collaborative team where you will grow your engineering depth and applied machine learning skills while delivering solutions used in real workflows. Bring your Python and data-driven mindset to design, launch, and continuously improve high-quality AI capabilities.

As an Data Scientist Associate at JPMorgan Chase , you will design, build, and operate production generative AI solutions that deliver measurable business outcomes. You will translate ambiguous business needs into reliable AI services by combining enterprise data with large language models and well-instrumented workflows. You will own delivery from solution design through deployment, monitoring, and continuous improvement. You will partner closely with engineering, machine learning, and data teams to meet defined quality, stability, and control expectations.

Job Responsibilities

• Design production generative AI services that use enterprise data to solve defined business problems with measurable outcomes.
• Build large language model workflows including prompt orchestration, retrieval-based augmentation, structured outputs, and tool or function calling.
• Develop agent-based AI workflows with multi-step orchestration, state management, and controls that support safe, controlled execution.
• Own end-to-end delivery from solution design and data preparation through evaluation, deployment, monitoring, and iteration.
• Implement evaluation frameworks using offline test sets, automated regression testing, and structured human review to make quality measurable and repeatable.
• Apply data analysis techniques (for example, trend analysis, anomaly detection, and experimentation) to diagnose model behavior and improve system performance.
• Build data pipelines that clean, transform, and aggregate data from multiple sources using Python and structured query language.
• Deliver containerized services to Kubernetes with automated continuous integration and continuous delivery pipelines, environment promotion, and rollback strategies.
• Monitor service health and model performance using observability practices, and drive remediation through well-defined operational processes.
• Communicate progress, risks, and results to technical and non-technical stakeholders with clear updates tied to business impact.

Required qualifications, capabilities, and skills

• Bachelor’s degree (or equivalent practical experience) in computer science, data science, engineering, statistics, or a related quantitative field.
• Four or more years of hands-on experience using Python to build data-driven or machine learning-enabled solutions in production environments.
• Hands-on experience building generative AI applications using large language model application programming interfaces, including advanced prompting, retrieval-based augmentation, and structured outputs.
• Practical experience building multi-step, tool-using AI workflows using common agent frameworks, including implementing controls for hallucinations, prompt injection, and data leakage.
• Proficiency with relational databases and structured query language; experience with MySQL, Oracle, or PostgreSQL.
• Working knowledge of core machine learning concepts such as regression, classification, feature engineering, model evaluation, and experimentation.
• Proven experience designing and maintaining automated build, test, and deployment pipelines across environments.
• Hands-on experience containerizing applications and deploying production workloads on Kubernetes.
• Strong written and verbal communication skills, including the ability to translate technical work into stakeholder-ready outcomes.

Preferred qualifications, capabilities, and skills

• Experience across the full software development lifecycle, including design, testing, deployment, and operational stability.
• Experience operating production workloads on Amazon Web Services, including managed services and cloud-native practices.
• Working knowledge of front-end development (for example, React) for prototypes or lightweight user interfaces.
• Prior exposure to technology risk, controls, or compliance partnership in regulated environments.
• Familiarity with model observability practices, including quality monitoring and checks for bias and hallucinations.