We are seeking a talented individual to join our Underwriting Analytics team at Guy Carpenter. This role will be based in any of Guy Carpenter's offices. This is a hybrid role that has a requirement of working at least three days a week in the office.
The Underwriting Analytics team develops analytical solutions that support underwriting strategy, portfolio insights, risk segmentation, and decision support across the business. The successful candidate will work at the intersection of data science, underwriting, and business strategy, partnering closely with technical and commercial stakeholders to deliver scalable insights and tools.
We will count on you to:
- Develop and enhance analytical models, decision tools, and data products that support underwriting and portfolio management objectives.
- Apply statistical, machine learning, and data mining techniques to large internal and external datasets to identify patterns, performance drivers, and emerging risk insights.
- Partner with underwriting, broking, actuarial, catastrophe modeling, and portfolio management teams to translate business questions into scalable analytical solutions.
- Build models and analytical frameworks for use cases such as risk segmentation, portfolio monitoring, performance attribution, propensity analysis, anomaly detection, and underwriting optimization.
- Design and execute exploratory data analysis to assess portfolio characteristics, underwriting outcomes, loss experience, and market trends.
- Engineer features from structured and semi-structured insurance datasets, including exposure, submission, pricing, claims, and third-party data sources.
- Support productionization of analytical workflows by contributing to model validation, documentation, version control, testing, and monitoring.
- Create clear and compelling visualizations, summaries, and presentations that communicate technical findings to both technical and non-technical stakeholders.
- Contribute to the continuous improvement of underwriting analytics capabilities, data pipelines, and modeling standards across the team.
- Stay current on emerging methods in machine learning, optimization, and applied analytics relevant to underwriting and reinsurance decision-making.
What you need to have:
- Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Engineering, or another quantitative discipline.
- 3–6 years of relevant experience in data science, advanced analytics, or quantitative modeling.
- Prior insurance industry experience at a carrier, reinsurer, broker, or data/analytics vendor.
- Demonstrated experience working with insurance datasets and business problems.
- Strong programming skills in Python and SQL; familiarity with R is a plus.
- Experience building and validating predictive or diagnostic models using statistical and machine learning techniques.
- Strong grounding in probability, statistics, model evaluation, and analytical problem-solving.
- Ability to communicate technical concepts effectively to underwriting and business stakeholders.
- Strong attention to detail, sound judgment, and the ability to manage multiple priorities.
What makes you stand out:
- Experience specifically in underwriting analytics, portfolio analytics, or insurance performance analytics.
- Familiarity with commercial insurance and/or reinsurance concepts, underwriting workflows, and portfolio management practices.
- Experience with methods such as GLMs, tree-based models, clustering, regularization, time series analysis, and anomaly detection.
- Experience with exposure, policy, claims, pricing, or submission data in a P&C or specialty insurance context.
- Familiarity with cloud-based analytics environments, model deployment practices, and modern data tooling.
- Exposure to geospatial, catastrophe, or alternative risk datasets is a plus.
- Advanced degree in a quantitative field
Why join our team:
- We help you be your best through professional development opportunities, interesting work and supportive leaders.
- We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.
- Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.
Skills Required
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Engineering, or another quantitative discipline.
- 3-6 years of relevant experience in data science, advanced analytics, or quantitative modeling.
- Prior insurance industry experience at a carrier, reinsurer, broker, or data/analytics vendor.
- Demonstrated experience working with insurance datasets and business problems.
- Strong programming skills in Python and SQL.
- Familiarity with R.
- Experience building and validating predictive or diagnostic models using statistical and machine learning techniques.
- Strong grounding in probability, statistics, model evaluation, and analytical problem-solving.
- Ability to communicate technical concepts effectively to underwriting and business stakeholders.
- Strong attention to detail, sound judgment, and the ability to manage multiple priorities.
- Experience in underwriting analytics, portfolio analytics, or insurance performance analytics (GLMs, tree-based models, clustering, time series, anomaly detection).
- Familiarity with commercial insurance and/or reinsurance concepts, underwriting workflows, and portfolio management practices.
- Experience with exposure, policy, claims, pricing, or submission data in a P&C or specialty insurance context.
- Familiarity with cloud-based analytics environments, model deployment practices, and modern data tooling.
- Exposure to geospatial, catastrophe, or alternative risk datasets.
- Advanced degree in a quantitative field.
Marsh McLennan Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Marsh McLennan and has not been reviewed or approved by Marsh McLennan.
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Leave & Time Off Breadth — Leave offerings are described as generous, including sizable PTO, paid holidays, paid sick days, and additional time off such as paid volunteer time and “Summer days.” These time-off benefits are portrayed as a standout part of the overall rewards package.
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Healthcare Strength — Healthcare coverage is characterized as comprehensive, spanning medical, dental, and vision options, with additional supports like disability and life insurance and access to mental health resources and an EAP. The breadth of plan options is positioned as a core strength of the benefits package.
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Retirement Support — Retirement benefits are framed as solid, with 401(k) programs and employer matching frequently highlighted alongside other financial programs. Stock purchase options are also referenced as an additional wealth-building component of the total rewards mix.
Marsh McLennan Insights
What We Do
Marsh McLennan (NYSE: MMC) brings together nearly 78,000 experts in risk, strategy, and people across Marsh, Guy Carpenter, Mercer, and Oliver Wyman, serving clients in over 130 countries. Marsh enables enterprise worldwide by helping clients manage risks, transforming uncertainty into opportunity. Guy Carpenter helps clients grow profitably with reinsurance broking expertise, advisory services, and advanced analytics. Mercer helps organizations advance the health, wealth, and careers of their most vital asset — their people. Oliver Wyman’s expertise in strategy, operations, risk, and organization transformation changes what is possible for our clients, their industries, and society. Together, we combine a unique range of capabilities to help our clients solve problems, seize opportunities, and build lasting success in increasingly complex operating environments.







