Lead Data Scientist - Healthcare

About The Role

HiCounselor is assisting one of our clients in hiring a Lead Data Scientist – Healthcare to spearhead data science initiatives in the healthcare domain. The ideal candidate will be responsible for leading end-to-end projects, applying advanced analytics and AI to healthcare data, and delivering insights that drive strategic decision-making. This role also involves mentoring team members, collaborating with stakeholders, and ensuring data-driven solutions are effectively integrated to support business objectives and improve healthcare outcomes.

Visa Sponsorship: Not available

Key Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
• Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
• Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
• Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
• Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals.
• Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments.

Preferred Qualifications

• PhD or Master’s degree in Computer Science, Machine Learning, or related field.
• 8+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models.
• Deep expertise in:
• LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
• Graph-based retrieval systems (GraphRAG, knowledge graphs)
• Embedding models (e.g., BGE, E5, SimCSE)
• Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
• Document segmentation and preprocessing (OCR, layout parsing)
• Distributed training frameworks (NCCL, Horovod, DeepSpeed)
• High-performance networking (InfiniBand, RDMA)
• Model fusion and ensemble techniques (stacking, boosting, gating)
• Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
• Symbolic AI and rule-based systems
• Meta-learning and Mixture of Experts architectures
• Reinforcement learning (e.g., RLHF, PPO, DPO)

Bonus Skills

· Experience with healthcare data and medical coding systems (e.g., CPT, CM, PCS).

· Familiarity with regulatory and compliance frameworks in AI deployment.

· Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc – production.

Preferred Qualifications:

· M.S. or PhD in a computational domain

· Publication history in deep learning or statistical domain

· Experience with SQL databases and query language

· Experience in AzureML, AWS, or cluster computing architectures

· Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches

· Experience with XML and XSLT

· Healthcare domain background

· IT full-stack engineering experience

Pay: A reasonable estimate of the current range is: $150,000 - $165,000. In addition, you may be eligible for a discretionary bonus for the current performance period.

Pay: $150,000.00 - $165,000.00 per year

Benefits:

• 401(k)

Work Location: Remote (CST preferred)