VP of Applied AI

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Explore how you can contribute at AmeriLife.

For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.

Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.

Job Summary

Reporting directly to the Chief Data Officer, the Vice President of Applied AI will spearhead AmeriLife's enterprise-wide Artificial Intelligence strategy. This executive role is crucial in establishing an "AI first" vision and culture, transforming how AmeriLife leverages data and intelligence across its value chain. The ideal candidate will build and scale AI capabilities, define the AI roadmap, and ensure the rapid development and deployment of AI solutions. The goal is to enhance agent productivity, optimize distribution channels, personalize experiences, improve operational efficiency, mitigate risks, and unlock growth opportunities. The role requires strong business collaboration skills to work effectively with various stakeholders and ensure alignment with business objectives. Success will be measured by the adoption rate and impact of AI initiatives across AmeriLife.

Job Description

Key Responsibilities

• Define, articulate, and champion a clear, actionable AI strategy and multi-year roadmap aligned with AmeriLife's business objectives.
• Identify, evaluate, and prioritize AI opportunities across all business functions, focusing on initiatives that deliver competitive differentiation and quantifiable ROI.
• Collaborate closely with executive leadership and business unit heads to secure buy-in, allocate resources, and ensure strategic alignment for all AI initiatives.
• Facilitate cross-functional collaboration to integrate AI solutions seamlessly into business processes and drive organizational change.

• Lead the full lifecycle of AI/ML solution development: from ideation and design through development, testing, deployment, and maintenance.
• Recruit, build, mentor, and lead a high-caliber, cross-functional team comprising AI/ML engineers, data scientists, and AI product or project managers.

• Cultivate a dynamic team culture centered on innovation, rapid iteration, collaboration, continuous learning, and ethical AI practices.

• Forge strong partnerships with business units, especially core distribution channels, to co-create AI solutions that address specific needs and drive adoption.
• Develop and execute comprehensive change management strategies to facilitate the smooth adoption of AI technologies.

• Provide regular, transparent reporting on AI program progress, outcomes, and challenges to executive leadership and key stakeholders.
• Utilize performance data and feedback loops to continuously refine the AI strategy, prioritize initiatives, and optimize deployed solutions.

Qualifications

• An advanced academic degree (Master's or PhD) in a quantitative field such as Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Physics, Operations Research, or a related Engineering discipline is strongly preferred
• 15 years of experience in data science, applied AI/ML, and advanced analytics, with a proven track record of developing and executing AI strategies that drive significant business transformation and ROI.
• 8 years in leadership roles, including team building, mentoring, and retention, demonstrating the ability to recruit, develop, and lead high-performing AI/ML teams.
• Expertise in aligning AI initiatives with business goals, facilitating cross-functional collaboration, and integrating AI solutions into existing processes.

Technical Expertise

• Programming Skills: Expert-level proficiency in Python and SQL for data manipulation and querying.
• MLOps Expertise: Proven experience in designing, implementing, and managing robust MLOps pipelines for automated model training, validation, versioning, deployment, performance monitoring, and governance.
• Machine Learning Libraries: Proficiency in using machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras for model development and experimentation.
• Cloud-Based AI Services: Experience with cloud-based AI services and tools, such as AWS SageMaker, Google AI Platform, and Azure Machine Learning, to streamline AI development and deployment.

Preferred Qualifications

• Experience within the insurance or financial services industry, demonstrating a deep understanding of industry-specific challenges and opportunities.
• Understanding of core business operations, financial principles, and the insurance distribution landscape, enabling effective alignment of AI initiatives with business goals.