SVP of Actuarial Engineering

JOB OVERVIEW:

A unique opportunity for credentialed actuaries to transition into a technology solutions architect role and provide actuarial analytics tools for client executives and brokers that inform the business values chain of the deal cycles.

We are looking for an SVP of Actuarial Engineering who is, first and foremost, an experienced actuary in insurance (REQUIRED) who builds analytics products that non-actuaries can use.

This is not a people management role in the traditional sense. You will be hands-on building, validating, and operationalizing actuarial models across EPIC's full commercial lines and benefits portfolio. You will work with a lean team and partner closely with solution engineers to move models from development into production on Azure Databricks platform.

Equally important, you will translate complex actuarial outputs into clear, executive-ready presentations and documents. Your models will inform brokers in benchmarking risk, validating program pricing, and advising clients. You will also develop training materials and translate the solutions to practice leaders, client executives, and brokers.

If you are a credentialed actuary comfortable in Python coding and seeking to embrace modern tools and platforms for building scalable intelligent solutions, perhaps this is where that trajectory begins!

WHAT YOU’LL DO:

What you'll own:

• Actuarial model library across D&O, Cyber, General Liability, Professional Liability, Property, and Employee Benefits — frequency-severity models, loss development, layer pricing, limit adequacy, and program benchmarking.
• Collaboration with other actuaries to translate actuarial model outputs into client-ready deliverables and executive presentations that brokers and practice leaders can use directly in deal cycles.
• Operationalization of models built by other teams in the enterprise taking validated models through SDLC into production in collaboration with data & application engineering partners.
• Quality and auditability standards for all actuarial analytics outputs consumed by client-facing teams.

Responsibilities:

• Design, build, and validate actuarial and statistical models for commercial lines pricing and portfolio analytics using Python.
• Build and develop a team of actuarial professionals, leading from the front as a hands-on builder while scaling the team's modeling, analytics, and advisory capabilities across the platform.
• Develop reusable analytics solutions that translate complex model outputs into board-ready presentations and client-facing analytical narratives.
• Partner with solution engineering to operationalize models on governed platforms, owning the handoff from development through production within a structured SDLC process.
• Manage and direct onshore and offshore vendor resources, providing domain guidance, quality review, and delivery oversight.
• Collaborating with solutions leads on architectural decisions affecting client-facing actuarial deliverables.
• Engage with practice leaders and client executives to embed platform deliverables into new and renewal business workflows, refining models and presentation layers based on stakeholder feedback.
• Maintain model documentation, validation records, and auditability standards for client-facing and regulatory contexts.
• Contribute to the analytics roadmap by identifying opportunities across lines of business for innovative, data-driven insights.

WHAT YOU’LL BRING:

Credentials & Technical Skills

• Actuarial Credentials — FCAS or ACAS preferred. Credential requirements are flexible for candidates with proven commercial lines brokerage business depth and a strong delivery-track record.
• Python — Production-level Python for actuarial modeling, statistical analysis, and data manipulation. You write clean, maintainable code not one-off scripts.
• SDLC Fluency — Working understanding of version control, testing, documentation, and engineering handoff as applied to model deployment. You don't need to be a software engineer, but you need to speak the language.
• Data & Cloud — Comfort with large policy, claims, and exposure datasets. Working knowledge of Databricks, Azure, or equivalent platforms sufficient to collaborate with solution engineering partners.
• Communication — Proven ability to build executive-level presentations and translate actuarial complexity into clear business narratives. Your work creates C-suite and board level deliverables.

Experience & Education

• 7+ years of actuarial experience with at least 3 years in commercial-lines brokerage, carrier, or consulting environments. FCAS or ACAS preferred.
• Bachelor's degree in actuarial science, mathematics, statistics, or related quantitative field required; advanced degree preferred
• Demonstrated experience building actuarial models in Python — candidates who work exclusively in Excel or R will need to show a credible transition path
• Prior exposure to brokerage analytics context preferred — understanding how actuarial outputs are used in placement and renewal cycles, not just pricing or reserving

Domain

• Working knowledge of commercial lines insurance across D&O, Cyber, GL, PL, Property, and Employee Benefits — coverage structures, program towers, exposure bases, and the actuarial concepts underlying each
• Familiarity with how benchmarking, limit adequacy analysis, and TCOR modeling are used in client advisory and renewal negotiations
• Comfort working across a matrixed organization with brokers, practice leaders, client executives, client-facing actuarial departments, and technical teams simultaneously.

LOCATION:

Hybrid is preferred out of any of our office locations but open to fully remote for qualified candidates.