# WTW appoints Lizzie Eason as Director and Actuarial Data Scientist > Source: > Published: 2026-06-25 05:48:23.647641+00:00 ### What happened WTW appointed **Lizzie Eason** as Director and Actuarial Data Scientist within its Pricing, Product, Claims and Underwriting (PPCU) consulting team in North America, effective **June 15, 2026**, according to Insurance Journal. Reinsurance News reports she will report to **Dale Porfilio**, Senior Director and Head of Business Development, Personal and Commercial Lines, within the Insurance Consulting and Technology business. Reporting by Insurance Journal and Reinsurance News states Eason joins from roles including Principal Data Scientist at **Everest** and data science consulting at **Nationwide**, and that she holds Associate status with the **Casualty Actuarial Society (ACAS)**. ### Technical details According to Insurance Journal and Reinsurance News, Eason's remit will emphasise **predictive modelling**, **advanced analytics** and the application of **artificial intelligence (AI)** across pricing, underwriting, claims and portfolio management. Sources say her role includes developing integrated consulting and technology solutions and helping insurers design and implement analytical frameworks and data capabilities. The reporting frames these activities as client-facing consulting and implementation work rather than an internal product launch. ### Editorial analysis Industry pattern observations: advisory firms and consulting teams are increasingly hiring senior hires with combined actuarial and data science skills to bridge model development and operational deployment. Companies undertaking comparable hires typically aim to shorten the gap between prototyped models and productionized decision processes, including embedding model governance and scalable data pipelines. ### Context and significance For practitioners: this appointment reflects continued demand from carriers for advisers who can pair actuarial rigor with machine learning tooling. Observers following the sector will note that hires with carrier-side experience, like Eason's nine years, are valuable when consultancy engagements require integration with legacy actuarial processes and regulatory reporting. The quoted comment from Dale Porfilio highlights the business framing used in public coverage: "Lizzie combines deep insurance experience with strong data science expertise. Her ability to translate complex analytics into practical applications will be a major asset to the ICT team as we continue to build on our strong track record in personal and commercial lines and deliver solutions that generate exceptional value to our clients," (Reinsurance News; InsurTechEye). ### What to watch Observers and practitioners should monitor WTW client-facing output in the coming quarters for case studies or service offerings tied to AI-enabled pricing, claims analytics, or deployment tooling. Industry-pattern indicators to follow include published white papers, webinar case studies, or partnership announcements that demonstrate movement from advisory models to implemented analytics workflows. ### Editorial analysis - practical takeaway For data science and actuarial teams in carrier organisations, the trend of consulting hires with production deployment experience suggests that external advisory engagements may increasingly include implementation support. Teams evaluating vendors should clarify expectations around model governance, data engineering responsibilities, and how predictive models will be operationalised during procurement conversations. ## Scoring Rationale A single director-level hire at an insurance consulting firm with an actuarial data science remit. Reflects market demand for applied ML in insurance pricing but is a minor personnel move with no new technology or capability announcement. Score adjusted from 5.6 to 3.8; sources re-ranked with generic category/pagination pages demoted to end. Practice with real Health & Insurance data 90 SQL & Python problems · 15 industry datasets 250 free problems · No credit card [See all Health & Insurance problems](/problems/datasets/health)