Lead Data Scientist

Responsibilities:

• Own the data problem formulation for the company's AI systems, defining how historical decision data, claim outcomes, and domain signals are structured, labeled, and mined to drive model accuracy and continuous improvement.
• Lead the design of training data strategies: extracting signal from complex healthcare datasets, building high-quality labeled datasets from real-world coding decisions and denial outcomes, and prioritizing annotation effort for maximum model impact.
• Drive high-cardinality data mining across large code and payer spaces, identifying rare but high-value patterns that improve autonomous coding performance.
• Own the architecture and evolution of the company's core AI platform, spanning real-time inference, batch processing, and model training pipelines across traditional ML and GenAI workloads.
• Architect multi-modal AI systems combining unstructured clinical text, structured EHR data, and domain knowledge for comprehensive healthcare coding automation.
• Lead technical evaluation of AI tools, platforms, and vendors; own build vs. buy decisions for key AI infrastructure components.
• Mentor and develop AI engineers across NLP, traditional ML, and Gen AI specializations; lead architecture reviews and establish engineering best practices.
• Partner with data engineering, GenAI, NLP, and product teams to align data and model training strategy with product requirements.

Requirements:

• 8+ years building and deploying production AI/ML systems, with demonstrated emphasis on solving the underlying data problem, not just model selection or infrastructure.
• Deep experience working with complex, domain-specific data at scale: extracting signal from noisy real-world datasets, building training data from historical decision records, and handling high-cardinality label spaces.
• Deep expertise across multiple AI domains (NLP, traditional ML, and GenAI) with production deployments in each.
• Expert-level proficiency in PyTorch or TensorFlow; experience with model training at scale.
• Proven track record on AI/ML projects where data quality, labeling strategy, and training data curation were the primary challenges, not just model architecture or deployment.
• Experience with cloud-native AI architectures on AWS, Azure, or GCP; containerized deployments (Docker, Kubernetes) for AI workloads.