Principal Data Scientist – Healthcare & Life Sciences AI, Azure ML, Databricks

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CTI Staffing

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 1998

🎯 Recruiter

🤝 B2B

Recruitment • B2B

CTI Staffing is a staffing and recruiting firm that specializes in onshore and nearshore talent solutions across the US, Canada and LATAM. The company places candidates in contract and permanent roles, offers services for both candidates and hiring organizations, and emphasizes cultural fit and long-term partnerships. With 25+ years of experience and a LATAM headquarters for nearshore engagement, CTI focuses on building teams quickly and supporting clients with tailored recruiting and staffing processes.

📋 Description

• Lead data science solutions for clinical analytics, predictive modeling, and population health use cases • Design ML workflows in Azure Machine Learning from 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 attention to 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

• 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 experience with experiment tracking, model management, deployment, and monitoring • Strong Databricks experience in data engineering, MLflow, Delta Lake, and collaborative data science • Solid foundation in predictive modeling, NLP, classification, regression, and experiment design • MLOps experience including CI/CD, version control, model registry, reproducibility, and governance • Familiarity with model transparency, AI risk management, and PHI-sensitive environments • Strong executive communication skills and ability to simplify technical concepts for non-technical stakeholders • Technical environment includes Azure Machine Learning, Databricks, MLflow, Delta Lake, Python, and cloud-native ML pipelines • Nice-to-have certifications or training: Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect Expert, Databricks Machine Learning Professional, Databricks Data Engineer, HIPAA, Responsible AI, or clinical analytics/governance training