Principal Data Scientist – Healthcare & Life Sciences AI (Azure ML/Databricks)

This is a remote position.

About This Opportunity

CTI Staffing is partnering with a leading healthcare and life sciences consulting practice to find a Principal Data Scientist for their AI and analytics team, fully remote.

This team helps healthcare organizations turn fragmented clinical and operational data into governed, production-ready AI solutions — moving clients from experimentation to measurable clinical and business impact. You'll work at the intersection of data science, clinical context, and cloud architecture, advising stakeholders while building solutions that hold up to healthcare-grade scrutiny.

What You'll Do

• Lead data science solutions for clinical analytics, predictive modeling, and population health use cases
  
• Design ML workflows in Azure Machine Learning — experimentation through deployment and monitoring
  
• Build ML and lakehouse solutions in Databricks using MLflow, Delta Lake, and scalable pipelines
  
• Translate clinical and business questions into modeling approaches, then validate and productionize them
  
• Assess data readiness and define feature strategies with an eye toward lineage and reproducibility
  
• Define model evaluation approaches covering performance, bias, explainability, and PHI considerations
  
• Advise clinical, technical, and executive stakeholders on AI adoption tradeoffs and risk
  
• Mentor data scientists and contribute reusable healthcare ML patterns and delivery accelerators
  

Requirements

What You Bring

Must-Have:

• 10+ years in data science, ML, or healthcare analytics, ideally in a consulting/client-facing role
  
• Strong understanding of healthcare data, clinical workflows, and patient or operational data
  
• Hands-on Azure Machine Learning — experiment tracking, model management, deployment, monitoring
  
• Strong Databricks experience — data engineering, MLflow, Delta Lake, collaborative data science
  
• Solid foundation in predictive modeling, NLP, classification, regression, and experiment design
  
• MLOps experience — CI/CD, version control, model registry, reproducibility, governance
  
• Familiarity with model transparency, AI risk management, and PHI-sensitive environments
  
• Strong executive communication skills — able to simplify technical concepts for non-technical stakeholders
  

Nice-to-Have:

• Azure AI Engineer Associate, Azure Data Scientist Associate, or Azure Solutions Architect Expert
  
• Databricks Machine Learning Professional or Data Engineer certification
  
• HIPAA, Responsible AI, or clinical analytics/governance training
  

Technical Environment:

• Azure Machine Learning, Databricks, MLflow, Delta Lake, Python, cloud-native ML pipelines
  

What Success Looks Like:

• A production ML solution shipped and monitored, not just a prototype
  
• A repeatable healthcare ML delivery pattern the broader practice can reuse
  
• Clinical and executive stakeholders who trust and act on the model's outputs