Senior Data Scientist 2

Our client helps medical practices grow and scale by delivering exceptional patient experiences through cloud, data, and AI technologies. Leveraging deep clinical expertise and rich datasets, they build intelligent software that simplifies and automates clinical workflows. Their solutions improve operational efficiency while supporting high-quality patient care. With specialty-specific EHR, practice management, revenue cycle, and analytics platforms, they address the full needs of healthcare providers. Patient engagement, payments, and marketing tools further enhance practice performance. Today, they are trusted by over 40,000 providers to achieve clinical and operational excellence.

Responsibilities:

• Lead the design and development of LLM-powered solutions for clinical summarization, intelligent chat, and document understanding to improve patient care outcomes.
• Architect and implement end-to-end machine learning pipelines, including Retrieval-Augmented Generation (RAG) systems using vector databases such as Pinecone and Weaviate.
• Fine-tune and optimize state-of-the-art language models (e.g., GPT, LLaMA, Mistral) and apply advanced prompt engineering to address specialty-specific healthcare use cases.
• Own project delivery end to end, collaborating with stakeholders to ensure AI solutions are ethical, scalable, reliable, and well-governed.
• Mentor and support junior data scientists, fostering a culture of experimentation, learning, and innovation.

Requirements:

• Experience: 8–12 years in Data Science/ML, with at least 3+ years specifically focused on LLMs and Generative AI. 
• Technical Mastery: Deep hands-on expertise in Python and frameworks like PyTorch, Hugging Face, LangChain, or LlamaIndex. 
• Cloud Fluency: Proven ability to deploy models at scale using AWS SageMaker, Azure ML, or GCP Vertex AI. 
• MLOps Mindset: Experience with MLflow, Airflow, Docker, and Kubernetes to ensure our "bold innovations" are also stable and reliable. 
• Academic Foundation: A strong grasp of statistical analysis and evaluation techniques to mitigate hallucinations and ensure clinical accuracy.