Lead Data Scientist

Responsibilities:

• Own data problem formulation for AI systems using healthcare, claims, coding, and payer data.
• Design training data strategies, including data extraction, labeling, annotation, and quality improvement.
• Mine large, complex datasets to identify high-value patterns for improving AI/coding accuracy.
• Architect and evolve AI/ML platforms covering training, batch processing, and real-time inference.
• Build multi-modal AI systems using clinical text, structured EHR data, and domain knowledge.
• Evaluate AI tools/platforms and drive build vs. buy decisions.
• Mentor AI engineers across NLP, traditional ML, and GenAI.
• Collaborate with data engineering, GenAI/NLP, and product teams on data and model strategy.

Requirements:

• 8+ years building and deploying production AI/ML systems.
• Strong experience with complex, domain-specific data and high-cardinality datasets.
• Deep expertise across NLP, traditional ML, and GenAI, with production experience in each.
• Expert-level PyTorch or TensorFlow with experience in model training at scale.
• Strong hands-on experience with data quality, labeling, and training data curation.
• Experience with AWS/Azure/GCP and containerized AI workloads usingDocker/Kubernetes.