Data Scientist, Principal

About the 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.

Responsibilities

• Lead the development and deployment of novel applications that leverage generative AI models, setting the technical direction for AI and machine learning across the organization
• Design and develop scalable applications leveraging generative AI models including LLM applications, copilots, agents, and RAG and search, embedded in customer-facing products and enterprise workflows
• Rapidly prototype new features and iterate based on evaluation results
• Lead the architecture and development of new products and features from 0 to 1
• Collaborate with researchers and product managers to translate cutting-edge AI research into tangible product features
• Build the APIs, services, and data and retrieval pipelines that expose AI capabilities to applications
• Optimize software performance and ensure the reliability of deployed applications
• Champion best practices for building and deploying generative AI applications
• Evaluate model performance, analyze results, and implement improvements, ensuring responsible and compliant AI

Qualifications

• Bachelor’s degree in computer science, a quantitative discipline, or equivalent practical experience
• 8 years of experience in software development and applied AI/ML with a Bachelor’s degree; or 5 years with a Master’s; or a PhD with relevant experience
• 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 LangGraph
• Hands-on experience building applications that leverage generative AI models, including prompt engineering and retrieval-augmented generation (RAG)
• Experience with generative AI research or applications preferred
• Experience designing agent-based systems and orchestration frameworks preferred
• Experience 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
• Experience rapidly developing and shipping software in a fast-paced, customer-facing environment, adapting to changing priorities
• Understanding of responsible AI and governance for regulated or healthcare environments preferred