Based in either our Scottsdale, AZ or Chicago, IL offices, the AVP, Advanced Analytics & AI Strategy is the strategic and technical leader responsible for advancing AI‑driven intelligence across underwriting, claims, and operations to transform portfolio decision‑making into a data‑driven, co‑intelligence platform that delivers optimal, risk‑adjusted returns.

Reporting to the Chief Actuary, this role partners closely with the Senior Leadership Team, including the President, CUO, Underwriting Segment Leaders, CAO, and CCO, to co‑create analytics‑enabled portfolio strategies that balance growth and profitability and support shared accountability for portfolio‑level P&L outcomes.

The AVP leads a team responsible for the development and deployment of advanced analytics, machine learning, and intelligence solutions that inform portfolio segmentation, pricing adequacy, claim severity management, and operational efficiency.

This role ensures insights are operationalized into underwriting, claims, and portfolio levers that strengthen discipline, improve outcomes, and enable deliberate portfolio optimization across market cycles.

This position requires a hands‑on data science leader and change agent who drives adoption of intelligent tools, shapes enterprise AI strategy, and fosters a culture of disciplined, data‑driven decision‑making.

The AVP, Advanced Analytics & AI Strategy plays a critical role in defining and scaling the future of analytics across Vela.

• Lead the team in the design, deployment, and continuous improvement of intelligence solutions that deliver measurable outcomes across underwriting, claims, and operations.
• Develop and execute the analytics roadmap, including structured and unstructured data integration, variable selection, and alignment with Vela’s strategic priorities.
• Partner with the Senior Leadership Team to support portfolio oversight and optimize risk‑adjusted returns through analytics‑driven insights.
• Oversee the integration of intelligence into core underwriting and claims workflows, ensuring full leverage of Kalepa, Helix, and enterprise data assets.
• Champion adoption of data‑driven decision‑making through effective change management and stakeholder engagement.

• Partner with Underwriting to design, validate, and refine risk factors embedded within underwriting intelligence platforms to improve portfolio performance.
• Enhance risk scoring, pricing precision, portfolio targeting, and underwriting dashboards to support disciplined and profitable risk selection at both the risk and portfolio level.
• Partner with the CCO to advance claim analytics focused on handling efficiency, reserving accuracy, and claim settlement outcomes.
• Deliver insights on claim trends, early warning indicators, fraud detection, and leakage to inform underwriting, claims, and portfolio strategy.

• Advance portfolio management capabilities to support capital objectives, including the refinement of AI‑enabled, rules‑based engines.
• Enable dynamic portfolio management through continuous, forward-looking adjustment of risk selection, pricing, appetite and capacity based on risk insights and market conditions. 
• Establish dynamic underwriting audits at the individual risk and portfolio level.
• Provide senior leadership with clear, timely insights to support portfolio optimization and strategic decision‑making.

• Lead advanced analytics and machine learning initiatives, including model development, monitoring, recalibration, and continuous improvement.
• Build scalable intelligence capabilities that embed feedback loops into execution in partnership with EsTech, BTS, and DNA teams at Berkley.
• Drive operational efficiency by identifying process gaps, optimizing workflows, and implementing automation and intelligence solutions.
• Identify, prioritize, and develop AI use cases, including actionable prompts and intelligence tools that improve speed, quality, and consistency of execution.
• Partner with enterprise technology and data teams to ensure data quality, accessibility, architectural alignment, and adherence to enterprise standards.

• Bachelor’s or Master’s degree in Data Science, Statistics, Actuarial Science, Computer Science, Engineering, or related quantitative field
• 7–10+ years of experience applying data science, advanced analytics, or AI/ML in an insurance environment, with demonstrated impact on underwriting, claims, pricing adequacy, and portfolio performance
• Proven leadership experience building or managing analytics, data science, or operational‑efficiency teams.
• Expertise in SQL, Python, machine learning frameworks, and modern data platforms (e.g., Databricks, cloud‑based analytics environments).
• Strong understanding of underwriting, claims, risk modeling, or actuarial concepts preferred.
• Experience with dashboarding and reporting tools such as Power BI.
• Demonstrated ability to translate business needs into analytical solutions and communicate complex concepts to non‑technical stakeholders.
• Track record of driving change management, influencing cross‑functional teams, and implementing new technologies or processes.
• Strong problem‑solving, critical‑thinking, and strategic‑planning skills, with the ability to operate both hands‑on and at an enterprise‑strategy level.