Lead Data Scientist

Job Title: Lead Data Scientist – Healthcare (AI Architecture Focus) Location preference: EAST coast Key Responsibilities • Lead the design, development, and deployment of advanced data science and machine learning solutions within the healthcare domain. • Act as a technical lead, guiding data scientists and collaborating with AI Architects to ensure scalable, secure, and compliant AI systems. • Architect and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and deployment. • Work with structured and unstructured healthcare data such as EHR/EMR, claims, clinical notes, imaging metadata, and operational data. • Partner with stakeholders to define problem statements, success metrics, and data strategies aligned with clinical and business goals. • Ensure models comply with healthcare regulations and standards, including HIPAA and data privacy best practices. • Evaluate and recommend tools, frameworks, and cloud-based solutions for ML and AI workloads. • Oversee model monitoring, performance tuning, explainability, and governance in production environments. • Contribute to AI strategy, solution roadmaps, and architectural decisions alongside AI and platform teams. Required Qualifications • 7+ years of experience in data science, with recent experience in a Lead Data Scientist or similar senior role. • Strong experience working in the healthcare domain (payer, provider, life sciences, digital health, or health tech). • Hands-on experience designing and deploying ML models in production environments. • Solid understanding of AI architecture concepts, including scalable ML systems, MLOps, and cloud-based deployments. • Proficiency in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch). • Experience with SQL and big data technologies (e.g., Spark, Databricks, Snowflake). • Strong understanding of data modeling, statistics, and machine learning algorithms. • Experience working in cloud environments such as AWS, Azure, or GCP. • Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders. Preferred Qualifications • Prior experience collaborating closely with or acting as an AI Architect on enterprise-scale solutions. • Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Azure ML). • Familiarity with NLP, computer vision, or generative AI use cases in healthcare. • Knowledge of healthcare interoperability standards such as HL7, FHIR, or ICD codes. • Experience leading cross-functional teams in a contract or consulting environment.