M&A Actuary — P&C, FCAS, Remote/Hybrid

Marsh McLennan; New York (NY); Hybrid; USD 100,000 - 195,000; Full time

Benefits offered by this job

Performance-based bonuses

Benefits package

Job summary

Oliver Wyman is seeking a Consulting Actuary to join the US P&C M&A team. The role involves due diligence, data analysis, and modeling to support insurance and reinsurance transactions globally.

The candidate will lead projects, develop client-ready insights, and collaborate with cross-functional teams in the US and abroad. FCAS is required and proficiency in Python or R is preferred.

Qualifications

• BA/BS degree in Actuarial Science, Mathematics, Statistics, Economics, Finance or related field.
• 3+ years of professional actuarial experience in consulting or insurance.
• FCAS credential required.
• Broad exposure to P&C insurance in pricing, reserving, data & analytics, or risk management.

Responsibilities

• Execute actuarial due diligence projects for M&A and reinsurance transactions, reviewing actuarial reports, data and models.
• Quantify downside risk with stochastic models.
• Conduct exploratory data analysis on exposure/loss data and present findings.
• Develop executive level reports and data visualizations.
• Present findings to senior management and C-suite executives.
• Develop industry contacts through deals and conferences.
• Participate in client sales meetings and proposal development.
• Collaborate with US and global actuarial and management consulting practices.

Skills

FCAS credential

Python

R

Project leadership

Client engagement

M&A experience

P&C knowledge

Education

Actuarial Science degree

Tools

Python

R

Job description

Oliver Wyman is seeking a Consulting Actuary to join the US P&C M&A team. The role involves due diligence, data analysis, and modeling to support insurance and reinsurance transactions globally.

The candidate will lead projects, develop client-ready insights, and collaborate with cross-functional teams in the US and abroad. FCAS is required and proficiency in Python or R is preferred.