Property Actuarial Analyst

JOB OVERVIEW

We're hiring a Property Actuary to build the pricing models behind our property insurance work. You'll build, validate, and maintain actuarial and CAT models that tell us what property insurance should cost for our clients, and you'll turn that output into recommendations our brokers can use with risk managers and CFOs.

This is a hands-on modeling job. Most of your week will be spent in the data (policy info, exposures, loss runs) building and refining models that help brokers benchmark risk, sanity-check pricing, and advise clients heading into renewal. ACAS is preferred but not required. If you're a non-credentialed actuary with real property modeling chops, we want to talk to you.

If you like building property pricing models and want to see your work change how clients buy insurance, this one is for you.

In office requirement - 5 days a week in our EPIC office in New York NY.

• Build, validate, and maintain actuarial and statistical models we use to price property risk, both CAT and non‑CAT.
• Work with a collaborative group of 10+ actuaries, geographically dispersed, with a core group located in NYC.
• Use those models to produce indicated rates, layer pricing, limit adequacy reads, retention analysis, and TCOR views that feed real placement decisions.
• Get hands‑on with the data. That means cleaning up SOVs, loss runs, and policy schedules, and setting up workflows so the next analysis is faster than the last one.
• Turn model output into something a client can read. Think slides, benchmarks, and short written narratives our brokers can put in front of a CFO without translating.
• Sit with producers, property practice leaders, and clients to walk through the numbers, take the hard questions, and help shape program structure.
• Pitch in on new business. Request for Proposals, prospect meetings, and any deliverable where the actuarial story matters.
• Use AI to move faster and to push back on your own work. We expect you to lean on AI tools for the grunt work (data cleanup, first-pass code, drafting client narratives) and to stress-test your models with it: poke holes in assumptions, run sanity checks, and pressure-test results before they go out the door.
• Keep your models documented and auditable so anyone on the team can pick them up.

• Modeling & Simulation: @RISK, Model Risk
• CAT Platforms:
  Moody's RMS, Verisk AIR, Archipelago
• Data & Programming:
  Excel, VBA, Python, R
• Visualization & Mapping:
  Power BI, Tableau, ArcGIS
• Market Intelligence: S&P Capital IQ
• Presentations:
  Power Point
• AI:
  Copilot, Claude

Credentials & Technical Skills

• ACAS is preferred. If you're not credentialed, we'll still seriously consider you with at least 2 years of dedicated property pricing or modeling work.
• Real property modeling experience. You've worked on loss development, trend, frequency/severity, CAT load integration, and layer pricing, not just read about them.
• Comfort with big, messy datasets. Strong Excel is table stakes. You also work in at least one of Python, R, or SQL.
• You use AI in your day-to-day. Copilot, ChatGPT, Claude, whatever fits the job. You know where it saves you hours, and you know when not to trust it.
• You can explain your work without jargon. If you can't get a broker or a client to follow the logic, the model doesn't matter.

Experience & Education

• 2+ years of actuarial work, with meaningful time spent on property pricing or modeling.
• Bachelors in actuarial science, math, stats, or another quantitative field.
• Brokerage, reinsurance, or carrier-side property pricing experience is a big plus. Bonus points if you've seen how actuarial work actually moves a placement.
• Self-starter mindset. Our property analytics practice is growing fast, and we need people who want to help build it, not just slot into a process.

Domain

• You understand commercial property: how programs are structured, what a tower looks like, what TIV values actually represent, and the actuarial logic underneath it all.
• You know the major CAT models (AIR, RMS) and how vendor output gets folded into pricing. Hands‑on experience is great but not required.
• You've seen benchmarking, limit adequacy work, and…