AI Strategist

Wellabe

. Define Wellabe's AI strategy, investment themes, and roadmap aligned with strategic business goals .

Posted 10/16/2026full-timeDes Moines • Iowa • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and executing AI strategies aligned with business goals, leveraging extensive experience in AI/ML development, cloud ecosystems, and the insurance value chain. Proven ability to communicate complex technical concepts to diverse audiences while ensuring compliance and governance in AI initiatives.

Highest-signal resume keywords

AI Strategy DevelopmentMachine Learning ConceptsMicrosoft Azure EcosystemGenerative AI SolutionsInsurance Value Chain Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingMachine Learning DevelopmentPredictive ModelingPrompt EngineeringFeature EngineeringModel Performance MetricsAI/ML WorkflowsData AnalysisKPI DefinitionBusiness Case Development

Soft Skills

Exceptional CommunicationFacilitation SkillsCollaborationProblem FramingStakeholder Engagement

Tools & Technologies

AzureDatabricksMLflowAI/ML PlatformsAPIsNotebooks

Certifications & Qualifications

Bachelor's Degree in Data ScienceMaster's Degree or MBA Preferred

Industry Keywords

InsuranceFinancial ServicesDigital TransformationComplianceGovernanceModel Risk

Tech Stack

Tools & technologies

AzureCloudPython

About the role

Key responsibilities & impact

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 appropriate AI applications, quantify value, and prioritize opportunities
Lead cross-functional AI Intelligence Pods from discovery and business case development through experimentation, implementation, adoption, and value realization
Explore data, develop prompts, test LLM and machine-learning approaches, create proof-of-concepts, evaluate outputs, and determine technical feasibility
Use Python, notebooks, APIs, and AI/ML platforms to prototype ideas, analyze results, and communicate technical possibilities
Partner with Data Science, Analytics, Data Engineering, Architecture, Security, and IT teams to translate prototypes into scalable 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 covering privacy, security, fairness, explainability, model risk, human oversight, and appropriate enterprise-data use
Define KPIs and benefit-tracking approaches for AI initiatives
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 recommendations
Serve as an executive-facing thought partner and practitioner-level collaborator explaining AI trade-offs to business leaders and technical teams

Requirements

What you’ll need

7+ years of experience spanning AI/ML, AI strategy, product management, analytics, or digital transformation
3+ years in insurance or financial services required
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
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
Experience with 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
Ability to prototype and evaluate prompt engineering, RAG, embeddings/vector search, structured outputs, and AI agents
Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Business, or a related field
Advanced degree such as an MBA or MS, or equivalent advanced experience preferred

Benefits

Comp & perks

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