Full-Time

Actuarial Modeling Technical Lead

Updated on 9/12/2026

Marsh & McLennan

Marsh & McLennan

Risk, insurance and business consulting

Compensation Overview

$100k - $150k/yr

+ Performance-based incentives + Year-end bonuses

Company Does Not Provide H1B Sponsorship

Boston, MA, USA + 5 more

More locations: Toronto, ON, Canada | Dallas, TX, USA | Chicago, IL, USA | New York, NY, USA | Atlanta, GA, USA

Hybrid

At least three days per week in a local office or onsite with clients.

Category
Insurance (1)
Required Skills
Power BI
Microsoft Azure
Python
Regression
Software Testing
Data Visualization
R
Git
SQL
Machine Learning
Version Control
Matplotlib
Databricks
Data Analysis
Requirements
  • Actuarial credentials such as FCAS, FIA, or ACAS, or active progress toward a recognized actuarial qualification.
  • At least 3 years of property and casualty actuarial experience in consulting or insurance, with hands-on work in pricing, reserving, or claims analytics.
  • Demonstrated experience in property and casualty modeling and building predictive models, including regression-type, generalized linear model, generalized mixed model, and advanced machine learning models, for frequency/severity, loss cost, and other actuarial use cases.
  • Strong technical skills with actuarial tools and experience with Python, R, or SQL.
  • Excellent communication skills and the ability to translate complex technical work into clear business recommendations.
  • Intellectual curiosity, self-motivation, and the ability to deliver against tight deadlines in a client-facing consulting environment.
Responsibilities
  • Build, test, document, and maintain actuarial logic implemented in Python, R, or modeling platforms, ensuring reproducible, version-controlled workflows.
  • Design and implement end-to-end actuarial workflows across pricing, reserving, underwriting, and claims analytics, and create developer-ready specifications and technical documentation.
  • Leverage artificial intelligence-assisted development tools, cloud platforms, and machine learning algorithms to improve modeling, code generation, automated testing, and documentation while maintaining actuarial governance and correctness.
  • Perform exploratory data analysis, validate data pipelines using SQL, Databricks, and Azure, and turn complex datasets into clear recommendations with client-ready reporting, dashboards, and relevant visualizations.
  • Act as the liaison between actuarial subject matter experts and engineering teams by translating actuarial specifications into technical requirements, reviewing implementations, and validating them through unit and integration tests.
  • Contribute to practice tooling, best practices, and business development, and represent the practice at industry events.
Desired Qualifications
  • Experience with Git and GitHub.
  • Experience with Databricks.
  • Familiarity with cloud machine learning and data platforms, including Azure Machine Learning.
  • Familiarity with machine learning operations practices.
  • Experience with visualization tools such as Power BI, matplotlib, or ggplot.
  • Experience with dashboarding.

Marsh, formerly Marsh McLennan, is a global professional services firm focused on risk, insurance, reinsurance, talent and business strategy. Its operating businesses help organizations arrange coverage, model and transfer risk, design workforce programs and address complex management questions. Clients range from growing companies to governments and multinational enterprises. The group combines advisory expertise with insurance-market access and data, making it broader than an insurance broker alone while remaining primarily a business-to-business services organization.

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