Principal Data Scientist

Now is the time to bring your expertise to Insight. Healthcare and life sciences organizations are investing heavily in analytics, machine learning, and AI, but many still struggle to turn fragmented clinical and operational data into scalable, governed, production-ready solutions. 

We are seeking a Principal Data Scientist with deep clinical or healthcare and life sciences expertise, strong Azure Machine Learning experience, and hands-on Databricks capability. In this client-facing consulting role, you will help healthcare organizations design, develop, evaluate, and operationalize advanced analytics and AI solutions across modern cloud data platforms. 

You will work at the intersection of data science, clinical context, cloud architecture, and enterprise delivery. You will help clients move from experimentation to measurable business and clinical impact while ensuring solutions are secure, explainable, reproducible, and aligned to healthcare standards and stakeholder expectations. 

• Clinical Data Science Leadership: Lead the design and delivery of data science solutions for healthcare and life sciences use cases, including clinical analytics, predictive modeling, operational intelligence, population health insights, and workflow optimization. 

• Azure ML Solution Development: Design machine learning workflows using Azure Machine Learning, including experimentation, model training, model registry, deployment, monitoring, evaluation, and lifecycle management. 

• Databricks ML and Lakehouse Enablement: Build and guide ML solutions on Databricks, using notebooks, feature engineering, MLflow, Delta Lake, and scalable data pipelines to support healthcare AI and analytics use cases. 

• End-to-End ML Delivery: Translate business and clinical questions into data science problem statements, develop modeling approaches, validate outputs, and partner with engineering teams to productionize solutions. 

• Data Quality and Feature Readiness: Assess clinical and operational data readiness, identify data gaps, define feature strategies, and establish repeatable approaches for lineage, quality checks, and model reproducibility. 

• Responsible AI and Healthcare Governance: Define model evaluation strategies that address performance, bias, explainability, safety, drift, PHI considerations, and stakeholder trust. 

• Client Advisory and Stakeholder Engagement: Serve as a senior advisor to clinical, technical, and executive stakeholders, helping them understand tradeoffs, risks, value drivers, and practical adoption paths for AI and ML solutions. 

• Practice Enablement: Mentor data scientists and engineers while contributing reusable healthcare ML patterns, Databricks accelerators, Azure ML templates, and delivery best practices for Insight.