AI​/Data Scientist IV

Workforce Classification:

Telecommuter

Our purpose, to improve customers’ lives by making healthcare work better, is far from ordinary. And so are our employees. Working at Premera means you have the opportunity to drive real change by transforming healthcare.

To better serve our customers, we are fostering a culture that emphasizes employee growth, collaborative innovation, and inspired leadership. We are dedicated to creating an environment where employees can excel and where top talent is attracted, retained, and thrives. As a testament to these efforts, Premera has been recognized on the 2025 America’s Dream Employers list. Newsweek honored Premera as one of America’s Greatest Workplaces, America’s Greatest Workplaces for Inclusion, and America’s Greatest Workplaces for Mental Well-Being, Forbes ranked Premera among America’s Best Midsize Employers for the fourth time.

Learn how Premera supports our members, customers and the communities that we serve through our Healthsource blog:  .

As an AI/Data Scientist IV, you will apply advanced AI, machine learning, and analytical skills to the design, development, and deployment of AI solutions with measurable impact on the most challenging problems within healthcare. As a scientist, you understand business challenges, research and conceptualize AI‑enabled solutions, and are tenacious, practical, thorough, and successful in working collaboratively throughout the organization to bring them to life.

Additionally, you will measure and convey business value and results to stakeholders throughout the organization.

This is a telecommuter position, working from home.

• Develop and tune generative/agentic AI solutions aimed to solve highly complex problems within the delivery of healthcare or health plan services. Establish comprehensive tracking of experiments to manage the iterative process of building and testing models ensuring that AI solutions fulfill business requirements. Provide statistical rigor to these evaluations via the design of controlled tests to measure the impact of changes to the solution.
  
  Form and leverage strong collaborations with AI engineers to scale solutions for production grade performance.
• Develop and implement complex agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision‑making capabilities.
• Solve new business problems by reaching, designing, building, and validating complex or novel machine learning models. Conduct feature engineering and model optimization. Manage the complete lifecycle of machine learning models from conception to deployment while following SDLC and responsible AI practices.
• Consult with senior and executive level business leaders to scope, model, and recommend AI/ML, technical, or analytic solutions to highly complex problems within healthcare. Build and ensure consensus with stakeholders regarding the feasibility, tradeoffs, and delivery of recommended solutions.
• Research, recommend, and apply causal techniques (RCT, DiD, PSM, causal graph models, counterfactual reasoning, etc.) to estimate treatment effects to quantify or validate business benefit of complex health plan activities. Explain business impact to program owners and leadership using approaches that are meaningful to those audiences. Partner with program owner in the promotion or publications of results when applicable.
• Participate in code peer reviews and quality assurance testing. Troubleshoot issues as they arise to solve problems independently and collaboratively.
• Serve as the technical subject matter expert to establish best practices for the team and help coach the team via formal and informal initiatives.
• Maintain regular contact with customers through development cycle to ensure each step of implementation tracks customer’s needs.
• Lead analytics projects applying a working knowledge of Agile and SCRUM project methodologies.

• Bachelor’s Degree in Statistics, Computer Science, Programming, Data Science, or similar quantitative field.
• 8 years of experience in quantitative…