About the roleLifeMD is seeking a visionary and execution-focused Vice President of Data, Analytics & AI to lead the company's enterprise data and artificial intelligence strategy. Reporting to the Chief Technology Officer, this executive will build the next-generation data and AI platform that powers every aspect of LifeMD-from patient experiences and clinical operations to internal productivity, intelligent automation, and business decision-making.
This is a hands-on Data engineering leadership role for someone who has successfully taken data and AI initiatives from proof of concept to enterprise production. The ideal candidate is equally comfortable discussing distributed data architectures, Retrieval-Augmented Generation (RAG), AI agents, LLM orchestration, analytics strategy, data engineering vision, and executive business priorities.
You will lead multidisciplinary teams across Data Engineering, Analytics Engineering, AI Engineering, Machine Learning, and Business Intelligence while partnering closely with Product, Engineering, Clinical Operations, Marketing, Finance, Compliance, and Security.
What You Will OwnEnterprise Data Platform- Define and execute LifeMD's enterprise data strategy and roadmap
- Build and scale modern cloud-native data platforms supporting operational, clinical, product, and financial workloads
- Design robust batch and real-time data pipelines across healthcare, product, pharmacy, CRM, marketing, finance, and operational systems
- Establish enterprise data models, governance, metadata management, and data quality frameworks
- Enhance self-service analytics and trusted enterprise reporting
Artificial Intelligence & GenAI- Lead LifeMD's enterprise AI strategy and production deployment roadmap
- Build secure enterprise Retrieval-Augmented Generation (RAG) platforms leveraging proprietary healthcare knowledge and enterprise content
- Design, deploy, and manage AI agents that automate clinical, operational, customer support, engineering, finance, HR, and internal business workflows
- Establish scalable LLMOps and AI engineering practices supporting multiple foundation models and vendors
- Lead evaluation, experimentation, and production deployment of emerging AI technologies
- Develop AI governance frameworks focused on safety, explainability, privacy, compliance, and responsible AI adoption
Knowledge Platforms & Intelligent Automation- Build enterprise knowledge management capabilities supporting employees, providers, and customer-facing applications
- Develop internal AI copilots that improve productivity across engineering, customer care, operations, and clinical organizations
- Build intelligent search capabilities powered by vector databases and semantic retrieval
- Lead Model Context Protocol (MCP) architecture and governance to enable secure interoperability between AI models, enterprise systems, tools, and knowledge sources
- Drive automation initiatives that reduce manual work, improve operational efficiency, and accelerate decision-making
Data Engineering & Analytics- Lead enterprise data engineering and analytics teams
- Build scalable ELT/ETL pipelines supporting healthcare operations and business intelligence
- Develop executive dashboards and real-time operational insights across patient engagement, provider performance, finance, product, marketing, and growth
- Establish KPIs and measurement frameworks supporting customer experience, healthcare outcomes, operational efficiency, and business performance
- Enable experimentation, predictive analytics, forecasting, and AI-driven decision support
Leadership- Recruit, mentor, and develop high-performing teams across Data Engineering, Analytics Engineering, AI Engineering, Machine Learning, and Business Intelligence
- Foster a culture of experimentation, engineering excellence, innovation, continuous learning, and customer obsession
- Partner closely with Product Management, Engineering, Clinical Operations, Security, Legal, and Compliance to ensure scalable and compliant AI adoption
- Communicate technology strategy and business outcomes effectively to executive leadership and the Board of Directors
Requirements- 12+ years of experience leading enterprise data, analytics, AI, or machine learning organizations
- 5+ years leading large engineering organizations responsible for enterprise data platforms and AI systems
- Proven experience deploying Generative AI solutions from proof of concept through enterprise production
- Deep expertise building Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, enterprise knowledge repositories, and LLM orchestration frameworks
- Experience designing and deploying AI agents, workflow automation platforms, and intelligent assistants
- Strong understanding of MCP (Model Context Protocol), agent orchestration, prompt engineering, and modern AI application architectures
- Hands-on experience with modern cloud data platforms such as Snowflake, BigQuery, Databricks, or equivalent
- Strong programming experience in Python, SQL, APIs, distributed systems, and modern data engineering frameworks
- Experience with cloud-native architectures (AWS, Azure, or Google Cloud)
- Experience implementing LLMOps, MLOps, CI/CD, model evaluation, monitoring, observability, and governance frameworks
- Strong knowledge of healthcare data, HIPAA, PHI security, privacy, and regulatory compliance preferred
- Experience leading enterprise analytics, experimentation, KPI development, and executive reporting
- Exceptional communication, organizational, and executive leadership skills
Benefits- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (Roth 401k)
- Life Insurance (Basic, Voluntary & AD&D)
- Flexible PTO Policy
- Paid Holidays
- Short Term Disability
- Training & Development
