AI/ML Analyst

Location: Remote for candidates residing in Maryland, Washington, D.C., Pennsylvania, Delaware, West Virginia, Virginia, or North Carolina; the first 1–2 weeks are onsite in Elkridge, Maryland for training and collaboration.

What you will do

• Partner with business stakeholders to identify operational challenges and translate them into data, analytics, and business requirements.
• Analyze large and complex datasets to identify trends, risks, and improvement opportunities.
• Develop business cases and ROI narratives for analytics and AI initiatives.
• Collaborate with IT to create dashboards and visualizations and support the design, testing, and deployment of approved AI and machine learning use cases.
• Monitor deployed models for data quality issues, bias risks, and performance drift.
• Document data sources, logic, assumptions, and limitations while coordinating with IT, project management, compliance, operations, and external vendors.

Requirements

• Bachelor’s degree in Business Analytics, Data Science, Information Systems, Statistics, Computer Science, or a related discipline, or equivalent practical experience.
• 5+ years of experience in AI/ML data analysis, business analysis, or analytics roles within a healthcare insurance or payer environment.
• Hands-on experience with structured datasets and analytical tools for business decision-making.
• Strong SQL, data analysis, business intelligence, and visualization skills.
• Strong understanding of machine learning and AI concepts, including predictive modeling, NLP, and automation, with emphasis on business application rather than model development.
• Experience gathering requirements, writing technical and business documentation, and communicating insights and recommendations to non-technical audiences.

Nice to have

• Exposure to model governance, audit support, or regulatory reporting.
• Experience supporting analytics during high-volume periods such as Open Season.

Culture & Benefits

• Remote work arrangement after the initial onsite training and collaboration period.
• Cross-functional collaboration with IT, compliance, operations, business units, and external vendors.
• Focus on explainable, compliant, ethical, and responsible use of AI and analytics.