AI Strategist-Hybrid- Des Moines, Iowa

As an AI Strategist within the Chief Data & Analytics Office, you will lead how AI creates measurable business value across Wellabe while staying close enough to technology to turn strategy into working solutions. This role sits at the intersection of business strategy, AI product thinking, data and technology, and responsible innovation. It will translate Wellabe’s Capital, Growth, and Digital priorities into a focused portfolio of AI opportunities, then partner hands-on with cross-functional teams to validate, prototype, implement, and scale the highest-value use cases.

The ideal candidate is a strategist-practitioner: someone who can engage executives on where AI should be applied, build the business case and roadmap, and also work directly with data, models, prompts, prototypes, and AI platforms to determine what will actually work.

What you will accomplish:

• Turn enterprise business priorities into a pragmatic, prioritized AI strategy and roadmap tied to measurable outcomes.
• Accelerate profitable growth, operational efficiency, and better customer and agent experiences through AI.
• Rapidly validate ideas through hands-on experimentation and prototypes before committing to scaled delivery.
• Build reusable AI capabilities and patterns rather than disconnected, one-time solutions.
• Establish trust through responsible AI governance, transparency, human oversight, and measurable performance.
• Increase AI fluency and adoption across business teams.

• Define Wellabe's AI strategy, investment themes, and roadmap aligned with strategic business goals.
• Partner with Business Champions and functional leaders to frame business problems, identify where AI is appropriate, quantify value, and prioritize opportunities.
• Lead cross-functional AI Intelligence Pods from discovery and business case development through experimentation, implementation, adoption, and value realization.
• Operate hands-on during discovery and validation: explore data, develop prompts, test LLM and machine-learning approaches, create proof-of-concepts, evaluate outputs, and help determine technical feasibility.
• Use Python, notebooks, APIs, and AI/ML platforms to prototype ideas, analyze results, and communicate what is technically possible to business and engineering teams.
• Partner with Data Science, Analytics, Data Engineering, Architecture, Security, and IT teams to translate prototypes into scalable, supportable solutions in the Azure and Databricks ecosystem.
• Partner with AI Steering and AI Governance Committees to assess strategic alignment, value, feasibility, risk, readiness, controls, and sequencing.
• Establish reusable methods for AI opportunity assessment, experimentation, evaluation, deployment readiness, adoption, and value measurement.
• Guide responsible AI practices including privacy, security, fairness, explainability, model risk, human oversight, and appropriate use of enterprise data.
• Define KPIs and benefit-tracking approaches for AI initiatives, including baseline performance, adoption, operating impact, revenue or retention impact, and realized ROI where applicable.
• Evaluate AI vendors, foundation models, platforms, and emerging technologies through structured assessments and hands-on testing rather than vendor claims alone.
• Own the lifecycle of existing enterprise AI solutions, including monitoring, enhancement, and stakeholder support.
• Monitor developments in Generative AI, agentic AI, LLMs, conversational AI, predictive modeling, and intelligent automation and translate relevant developments into actionable recommendations for Wellabe.
• Serve as an executive-facing thought partner and a practitioner-level collaborator who can explain AI trade-offs clearly to both business leaders and technical teams.

• Working knowledge of traditional machine learning concepts including supervised learning, feature engineering, model performance metrics, explainability, monitoring, and model lifecycle management.
• Experience with cloud AI ecosystems, preferably Microsoft Azure and Databricks, including familiarity with MLflow and enterprise model/LLM lifecycle practices.
• Strong understanding of the insurance value chain, ideally including underwriting, claims, actuarial/pricing, policy administration, distribution, customer service, and retention.
• Ability to build business cases, define value hypotheses and KPIs, compare solution alternatives, and communicate technical and economic trade-offs to senior leaders.
• Ability to balance innovation…