AVP/VP, Underwriting Analytics

Marsh McLennan•Tampa, FL

2d•$105,000 - $192,500•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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

Responsibilities

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.