The Machine Learning Center of Excellence (MLCOE) at JPMorgan Chase leverages advanced machine learning methodologies alongside the firm’s distinctive data assets to enhance business decision-making across the organization. In this role, you will play a pivotal part in driving business performance through rigorous AI/ML model performance tracking and advanced data analysis of complex datasets to uncover trends, patterns, and correlations that inform actionable recommendations.

Job responsibilities

 Do periodical monitoring of MLCOE’s AI/ML models, tracking key performance indicators (KPIs). Generate deep insights through the analysis of data and understanding of business processes and turn them into actionable recommendations. Investigate and triage alerts related to model performance, data anomalies, or system failures, escalating as appropriate.Identify opportunities to enhance monitoring frameworks, automate processes, and improve operational efficiency.Collaborate with others in the organization to develop new ideas and brainstorm potential solutions.Prepare regular reports on model health, incidents, and remediation actions. Maintain up-to-date documentation of monitoring processes and findings.Develop presentations to summarize and communicate key messages to senior management and colleagues.  Ensure monitoring activities comply with regulatory requirements and internal model risk management policies.Support the deployment of model updates, including validation, testing, and post-deployment monitoring. Support MLCOE’s SOPs Management function as and when required.

Required qualifications, capabilities, and skills

Formal training or certification on AI/ML concepts and 2+ years applied experienceExperience in model monitoring, analytics, operations, or a related roleUnderstanding of AI/ML concepts and model lifecycle management.Experience with data analysis tools such as  Python, SQL, Excel. Strong data analysis and troubleshooting abilities.Excellent communication and documentation skills.Detail-oriented with strong organizational abilities.Ability to work collaboratively in a cross-functional, fast-paced environment.

Preferred qualifications, capabilities, and skills

Familiarity with model risk management frameworks and regulatory guidelines.Exposure to cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices.Bachelor’s / master’s degree in computer science, engineering, data science, or business.