Principal Data Scientist

Insight•Phoenix, AZ

3h•Remote

About The Position

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.

Requirements

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.
Strong understanding of healthcare data, clinical workflows, operational healthcare analytics, provider environments, patient data, or life sciences use cases.
Hands-on experience with Azure Machine Learning, including experiment tracking, model management, deployment patterns, monitoring, and integration with broader Azure services.
Strong experience with Databricks for data engineering, analytics, feature development, MLflow, Delta Lake, and collaborative data science workflows.
Strong foundation in predictive modeling, NLP, classification, regression, clustering, feature engineering, experiment design, and model validation.
Experience operationalizing ML models with CI/CD, version control, model registry, reproducibility, monitoring, and governance practices.
Familiarity with model transparency, AI risk management, PHI-sensitive environments, auditability, and healthcare-specific governance expectations.
Strong executive communication skills with the ability to simplify technical concepts and shape practical, business-aligned AI roadmaps.

Nice To Haves

Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect Expert, or relevant Microsoft AI and data certifications.
Databricks Machine Learning Professional, Databricks Data Engineer, or lakehouse-related certifications.
HIPAA, Responsible AI, clinical analytics, or AI governance training is a plus.

Responsibilities

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.
Design machine learning workflows using Azure Machine Learning, including experimentation, model training, model registry, deployment, monitoring, evaluation, and lifecycle management.
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.
Translate business and clinical questions into data science problem statements, develop modeling approaches, validate outputs, and partner with engineering teams to productionize solutions.
Assess clinical and operational data readiness, identify data gaps, define feature strategies, and establish repeatable approaches for lineage, quality checks, and model reproducibility.
Define model evaluation strategies that address performance, bias, explainability, safety, drift, PHI considerations, and stakeholder trust.
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.
Mentor data scientists and engineers while contributing reusable healthcare ML patterns, Databricks accelerators, Azure ML templates, and delivery best practices for Insight.