AI Strategist-Hybrid- Des Moines, Iowa

Wellabe•Des Moines, IA

3d•Hybrid

About The Position

As an AI Strategist within Wellabe’s Chief Data & Analytics Office, you will lead how AI creates measurable business value across the organization while staying close enough to technology to turn strategy into working solutions. The role sits at the intersection of business strategy, AI product thinking, data and technology, and responsible innovation. It translates Wellabe’s Capital, Growth, and Digital priorities into a focused portfolio of AI opportunities and partners with cross-functional teams to validate, prototype, implement, and scale high-value use cases. The ideal candidate is a strategist-practitioner who can advise executives on where AI should be applied, develop business cases and roadmaps, and work directly with data, models, prompts, prototypes, and AI platforms to assess what will work. The role leads AI initiatives from problem framing and experimentation through implementation, adoption, and value realization; builds reusable AI capabilities; advances responsible AI practices; and increases AI fluency and adoption across business teams. The position requires at least seven years of experience spanning AI/ML, AI strategy, product management, analytics, or digital transformation, including at least three years in insurance or financial services. A bachelor’s degree in a related field is required. An advanced degree or equivalent advanced experience is preferred. The role is hybrid and based in Des Moines, Iowa.

Requirements

At least 7 years of experience spanning AI/ML, AI strategy, product management, analytics, or digital transformation.
At least 3 years of experience in insurance or financial services.
Proven ability to define AI strategies and roadmaps and move initiatives from problem framing through experimentation, delivery, adoption, and measurable business outcomes.
Demonstrated hands-on AI/ML development experience, including independently building, testing, and evaluating working prototypes in Python, predictive models, and AI/ML workflows.
Ability to communicate model performance, assumptions, limitations, and business implications to technical and non-technical audiences.
Practical experience developing Generative AI and LLM solutions, including independently prototyping and evaluating prompt engineering, RAG, embeddings/vector search, structured outputs, and AI agents, and assessing their effectiveness, limitations, and suitability for production.
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 with governance, security, privacy, compliance, and model-risk expectations in a regulated environment.
Exceptional communication and facilitation skills, with the ability to influence executives, business leaders, product teams, and technical practitioners.
Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, Business, or a related field.

Nice To Haves

Advanced degree, such as an MBA or MS, or equivalent advanced experience.
Experience with Microsoft Azure and Databricks cloud AI ecosystems.
Familiarity with MLflow and enterprise model/LLM lifecycle practices.
Insurance value-chain knowledge, including areas such as underwriting, claims, actuarial/pricing, policy administration, distribution, customer service, and retention.

Responsibilities

Define Wellabe’s AI strategy, investment themes, and roadmap in alignment with strategic business goals.
Partner with Business Champions and functional leaders to frame business problems, identify appropriate AI opportunities, quantify value, and prioritize initiatives.
Lead cross-functional AI Intelligence Pods through discovery, business case development, experimentation, implementation, adoption, and value realization.
Conduct hands-on discovery and validation by exploring data, developing prompts, testing LLM and machine-learning approaches, creating proof-of-concepts, evaluating outputs, and assessing technical feasibility.
Use Python, notebooks, APIs, and AI/ML platforms to prototype ideas, analyze results, and communicate technical possibilities to business and engineering teams.
Partner with Data Science, Analytics, Data Engineering, Architecture, Security, and IT teams to transition 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.
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.
Serve as an executive-facing thought partner and practitioner-level collaborator, clearly explaining AI trade-offs to business leaders and technical teams.
Build AI fluency and adoption across business teams and develop reusable capabilities and patterns rather than disconnected, one-time solutions.

Benefits

401(k) with company match
Health insurance
Paid time off and holidays
Volunteer time off
Lifestyle Spending Account (LSA)
Paternity leave
LinkedIn Learning Premium access
CliftonStrengths® assessment and coaching
On-site and virtual workshops and cohorts
Free insurance acumen courses through AHIP and LOMA
Reimbursement and bonus opportunities for professional designations and certifications
Tuition reimbursement program
Mentorship programs