VP Analytics Engineering

Coaction Global•Morristown, NJ

About The Position

At Coaction, we’re a unique mix of leaders, achievers, thinkers, and team players with a high-performance mindset and a diverse skillset. We bring our industry expertise together to continually push the boundaries of what insurance can be for our clients. The Head of Data Analytics is responsible for leading enterprise analytics across Claims, Underwriting, Actuarial, Finance, Billing, and Reinsurance. This role combines strategic leadership with hands-on execution to deliver trusted business insights, standardized enterprise metrics, and production-ready advanced analytics solutions that directly improve underwriting profitability, claims efficiency, and financial performance.

Requirements

10+ years of experience in insurance analytics (Property & Casualty preferred).
Strong expertise in claims, underwriting, premium, exposure, and reserving concepts.
Advanced SQL skills (Redshift and SQL Server).
Hands on experience – AWS, PowerBI, AtScale(Similar Semantic Model)
Advanced SQL skills (Redshift and SQL Server).
Experience with Python or R for predictive modeling.
Demonstrated experience operationalizing analytics solutions into production environments.
Proven ability to partner with executive leadership and actuarial teams.

Responsibilities

Enterprise BI & KPI Governance
Establish and maintain standardized enterprise and have implemented the Semantic Layer, Semantic Model
Lead executive dashboard development and domain-level reporting.
Implement metric certification and governance processes.
Eliminate conflicting metric definitions across legacy and modern platforms.
Advanced & Predictive Analytics
Develop and deploy predictive models supporting claims severity, leakage detection, and fraud indicators.
Lead underwriting analytics initiatives including risk segmentation and portfolio optimization.
Partner with actuarial teams on trend modeling, development analysis, and scenario simulations.
Deliver reinsurance analytics including treaty impact and retention optimization analysis.
Ensure models are explainable, governed, production-ready, and measurable for business impact.
Analytics Engineering & Operationalization
Build and validate analytical datasets within Redshift ,SQL environments.
Partner with Data Engineering to productionize models and implement monitoring frameworks.
Establish reproducible analytics workflows and documentation standards.
AI & Automation Enablement
Identify and implement AI-driven opportunities in claims processing, underwriting decision support, and document automation.
Establish experimentation frameworks and responsible AI governance practices.
Support enterprise AI readiness through structured data standardization and quality improvements.
Leadership & Operating Model
Lead and mentor analytics team members across BI, analytics engineering, and data science.
Define intake, prioritization, and delivery standards.
Drive accountability, execution discipline, and business stakeholder alignment.