Data Scientist, Principal - AI Product Engineering

Your Role: The AI & Machine Learning team works in partnership across the enterprise to accelerate business outcomes by applying AI, machine learning, and generative AI to build intelligent products that create "intelligence at scale." Reporting to the Director, AI & Machine Learning, the Data Scientist, Principal will lead the development and deployment of novel applications that leverage generative AI models. This role focuses on rapidly developing new features and working across partner teams to deliver solutions and maximize impact, translating cutting-edge AI research into real-world products and taking features from 0 to 1. You will design, build, and ship production-grade AI products including LLM-powered applications, AI agents and copilots, retrieval-augmented generation (RAG) and search, and AI-enabled automation embedded directly into customer-facing applications and enterprise workflows such as claims, payment integrity, clinical insights, and member experience. You will set the technical direction for how AI is applied across the organization. Our leadership model is about developing great leaders at all levels and creating opportunities for our people to grow - personally, professionally, and financially. We are looking for leaders that are energized by creative and critical thinking, building and sustaining high-performing teams, getting results the right way, and fostering continuous learning. Your Knowledge and Experience Bachelor's degree in computer science, a quantitative discipline, or equivalent practical experience; Master's degree or PhD preferred 10 years of prior relevant experience in data science, machine learning, applied AI/ML, software engineering, or advanced analytics Proven track record of building and shipping software products rapidly, not just developing models or analyses Strong software engineering skills and proficiency in Python, including building APIs and backend services Experience leading ML design and optimizing ML infrastructure, model deployment, evaluation, and data processing, and working with machine learning frameworks and libraries Hands-on experience with deep learning and LLM application frameworks, including PyTorch, TensorFlow, LangChain, and LangGraphHands-on experience building applications that leverage generative AI models, including prompt engineering and retrieval-augmented generation (RAG) Experience with generative AI research or applications preferredExperience designing agent-based systems and orchestration frameworks preferredExperience with cloud computing platforms and infrastructure (e.g., Azure, Google Cloud, or AWS), and scalable data processing with SQL or Spark preferred Solid MLOps and LLMOps practices, including CI/CD, monitoring, and model lifecycle management preferredExperience rapidly developing and shipping software in a fast-paced, customer-facing environment, adapting to changing priorities preferred Understanding of responsible AI and governance for regulated or healthcare environments preferred HybridThis role requires employees to be in-office based on our hybrid workplace model, balancing purposeful in-person collaboration with flexibility. For most teams, this means coming into the office two days each week. Employees living more than 50 miles from an office location will work with their manager to determine in-office time based on business need.#LI-CM1