Principal Data Scientist

Location

You will have the flexibility to work fully remotely

Clinical / HLS, Azure ML and Databricks

You will have the flexibility to work fully remotely

• 14,000+ engaged teammates globally
• $8.2 billion in revenue in 2025
• Certified as a Great Place to work in 9 Countries in 2025
• Fortune 500 Company (No. 447) in 2025
• Received 25+ industry and partner awards in the past year
• $1.4M+ total charitable contributions in 2024 by Insight globally

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 product ionize 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.

• Experience:
  
  10+ years of experience in data science, machine learning, healthcare analytics, AI solution delivery, or enterprise data platforms, ideally in a consulting or client‑facing advisory role.
• Healthcare / HLS Expertise:
  Strong understanding of healthcare data, clinical workflows, operational healthcare analytics, provider environments, patient data, or life sciences use cases.
• Azure ML Expertise:
  Hands‑on experience with Azure Machine Learning, including experiment tracking, model management, deployment patterns, monitoring, and integration with broader Azure services.
• Databricks Expertise:
  Strong experience with Databricks for data engineering, analytics, feature development, MLflow, Delta Lake, and collaborative data science workflows.
• Applied ML Depth:
  Strong foundation in predictive modeling, NLP, classification, regression, clustering, feature…