Job DescriptionYour RoleThe AI & Machine Learning team works in partnership across the enterprise to accelerate business outcomes by applying machine learning, statistical analysis, generative AI, and applied AI to build intelligent products that create "intelligence at scale." Reporting to the Director, AI & Machine Learning, the Data Scientist, Consultant will develop and deploy novel applications that leverage machine learning models, statistical methods, and generative AI. 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, statistical models, and AI-enabled automation, embedded directly into customer-facing applications and enterprise workflows such as claims, payment integrity, clinical insights, and member experience.
ResponsibilitiesYour WorkIn this role, you will:
- Develop and deploy novel applications that leverage machine learning and generative AI models, contributing to the technical direction for AI, machine learning, and statistical analysis across the organization
- Design and develop scalable applications leveraging machine learning and generative AI models-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 and statistical analysis
- Contribute to the architecture and development of new products and features from 0 to 1
- Collaborate with researchers and product managers to translate cutting-edge AI and machine learning research into tangible product features
- Build the APIs, services, and data and retrieval pipelines that expose AI and machine learning capabilities to applications
- Optimize software performance and ensure the reliability of deployed applications
- Apply and promote best practices for building and deploying machine learning and generative AI applications
- Evaluate model performance using statistical analysis, analyze results, and implement improvements, ensuring responsible and compliant AI
- Provide technical guidance to less experienced team members, fostering a collaborative and high-performing environment
QualificationsYour Knowledge and Experience- Bachelor's degree in computer science, statistics, quantitative discipline, or equivalent practical experience
- 7 years of experience in software development, applied AI/ML, and statistical analysis
- Demonstrated track record of building and shipping software products rapidly, not just developing models or analyses
- Working knowledge of machine learning and statistical analysis methods, with experience applying these techniques to business problems
- Strong software engineering skills and proficiency in Python, including building APIs and backend services
- Experience with ML design and ML infrastructure, including 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)
Preferred Knowledge and Experience- Master's degree; or a PhD with relevant experience
- Experience with generative AI research or applications
- Experience building agent-based systems and working with orchestration frameworks
- Experience with cloud computing platforms and infrastructure (e.g., Azure, Google Cloud, or AWS), and scalable data processing with SQL or Spark
- 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
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.
Physical Requirements:Office Environment - roles involving part to full time schedule in Office Environment. Based in our physical offices and work from home office/deskwork - Activity level: Sedentary, frequency most of work day.
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