Senior Principal Data Scientist

The Sr. Principal Data Scientist ML/DL is a primary driver in the design and development of state-of-the-art Artificial Intelligence solutions for medical applications. The Principal Data Scientist ML/DL works closely with senior data scientists, machine learning engineers, software engineers and subject matter experts on current company technologies and forward-looking projects. They are the drivers of new research and solution implementation in the creation of novel artificial intelligence approaches.

Primary responsibilities include the enhancement of existing company NLP technologies and extension of those systems in new cloud-based applications. Emphasis is on development of novel machine/deep learning techniques for information extraction and synthesis. They translate research code into clinical NLP solutions deployed at scale in production environments including statistical methods, deep learning, and large language model technologies. Work will involve all aspects of methods development from initial PoC implementation to performance characterization and production launch of new methods.

The successful candidate will have a solid history of publication in Machine/Deep Learning with an emphasis on Natural Language Processing, Information Retrieval and/or Information Extraction. Exposure to recent research literature and the ability to effectively implement new technologies is key. The successful candidate will have proven success in taking machine/deep learning solutions to production environments. Solid technical skills are required.

Primary 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
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• PhD or master's degree in computer science, Machine Learning, or related field
• 15+ years of experience in applied AI/ML with statistics, with a solid track record of delivering production-grade models
• Experience with PyTorch
• Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
• Deep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture
• Solid knowledge of Python coding, SQL and database queries, data preparation, and analysis
• Knowledge of Azure ML and/or AWS
• Knowledge of exploratory Data Analysis (EDA)
• Knowledge of LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
• Knowledge of graph-based retrieval systems (GraphRAG, knowledge graphs)
• Knowledge of embedding models (e.g., BGE, E5, SimCSE)
• Knowledge of semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
• Knowledge of document segmentation and preprocessing (OCR, layout parsing)
• Knowledge of distributed training frameworks (NCCL, Horovod, DeepSpeed)
• Knowledge of high-performance networking (InfiniBand, RDMA)
• Knowledge of model fusion and ensemble techniques (stacking, boosting, gating)
• Knowledge of optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
• Knowledge of symbolic AI and rule-based systems
• Knowledge of meta-learning and Mixture of Experts architectures
• Knowledge of reinforcement learning (e.g., RLHF, PPO, DPO, GRPO), Supervised Fine Tuning (SFT), LoRA, QLoRA, axolotl
• Knowledge of Prompt optimization framework (AutoPrompt, GreaterPrompt, DSPy), GEPA
• Knowledge of algorithms and techniques within a computational domain with emphasis on text processing
• Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approaches
• Demonstrated publication record in AI domain especially relating to text extraction and summarization

Preferred Qualifications:

• 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