Analytics Solutions Associate

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 experience
• Experience in model monitoring, analytics, operations, or a related role
• Understanding 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.