SQL Senior Clinical AI Data Analyst (Philadelphia)

Do you like working with data and analytics to evaluate AI models and their performance?

Do you love collaborating with teams to assess and improve generative AI solutions?

About our Team

Elsevier is singularly focused on helping healthcare professionals improve clinical outcomes through evidence-based care. Our generative AI team works on evaluating and implementing cutting-edge AI technologies to enhance our healthcare solutions while ensuring their safety, reliability, and compliance with healthcare standards .

About the Role

We are seeking a motivated individual to join our dynamic generative AI evaluation team. We focus on assessing and validating AI models that support healthcare decision-making tools. Our team ensures that our AI solutions meet rigorous standards for accuracy, fairness, and clinical relevance. The successful candidate will help evaluate model performance, analyze output quality, and ensure our AI solutions align with healthcare best practices.

The successful candidate has relevant experience with AI model evaluation, including performance metrics analysis, output validation, and data visualization. They enjoy collaborating with cross-functional teams including AI researchers, product managers, clinicians, and engineers to evaluate AI models, analyze their outputs, and improve their performance in healthcare contexts.

Responsibilities

• Leading requirements gathering and development of analytics frameworks for evaluating generative AI models in healthcare applications
• Leading and defining the strategic direction for AI evaluation methodologies in their product area
• Managing the analytics roadmap for model evaluation. Collaborating with internal and external stakeholders to manage evaluation timelines and deliverables
• Defining, developing, and deploying queries and metrics to assess AI model performance and output quality
• Leading data model creation and maintenance for AI evaluation frameworks and monitoring systems
• Conducting comprehensive QA of AI outputs and participating in risk analysis and mitigation
• Understanding how security and privacy laws such as HIPAA affect AI deployment in healthcare. Understanding how to handle sensitive data including PHI and PII in AI contexts

Requirements