Data Scientist

This is a remote position.

We are seeking a highly skilled Data Scientist with expertise in Large Language Models (LLMs) and Natural Language Processing (NLP). In this role, you will lead the development and application of advanced LLMs to enhance our AI-driven solutions, drive innovation in language understanding, and enable meaningful insights from vast datasets. This position requires deep knowledge in data science, machine learning (ML), and the ability to fine-tune and deploy LLMs to solve real-world problems.

You will be working with cross-functional teams of healthcare professionals, data scientists, engineers, and business stakeholders to define and deliver cutting-edge solutions that transform healthcare delivery. If you are a highly motivated and results-oriented individual with a passion for leveraging healthcare data to drive insights and improve patient outcomes, we would love to hear from you.

Essential Duties And Responsibilities:

• Lead the design, development, and deployment of LLM-based solutions to address complex business challenges.
• Conduct advanced research and experiment with commercial LLM solutions (OpenAI GPT, Gemma, Llama), and leverage their APIs for rapid prototyping.
• Fine-tune pre-trained LLMs (e.g., GPT, BERT, T5) to suit specific use cases, ensuring high performance on natural language tasks.
• Utilize transfer learning techniques to adapt LLMs for domain-specific tasks.
• Develop and implement strategies for data augmentation and synthesis to improve model generalization.
• Collaborate with cross-functional teams (engineering, product management, and operations) to integrate LLMs into various applications, such as chatbots, knowledge retrieval systems, and data analytics platforms.
• Conduct advanced research in the field of LLMs to stay updated with cutting-edge methodologies and apply them to real-world problems.
• Build pipelines for preprocessing, training, and deploying models, ensuring scalability and reliability.
• Evaluate and optimize models using metrics such as accuracy, F1 score, perplexity, and computational efficiency.
• Create data pipelines for large-scale text data ingestion, cleaning, and annotation.
• Perform A/B testing and rigorous validation of LLM-based models, providing insights on model efficacy.
• Mentor junior data scientists and engineers on NLP techniques and best practices.
• Document model development and analysis processes for transparency and reproducibility.