AVP/VP, Underwriting Analytics

Posted Yesterday
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11 Locations
In-Office
105K-193K Annually
Mid level
Professional Services • Consulting
The Role
Develop and productionize underwriting analytics, predictive models, and decision tools using statistical and machine learning techniques. Partner with underwriting, broking, actuarial, and portfolio teams to perform feature engineering, exploratory analysis, model validation, visualization, and monitoring to support risk segmentation, portfolio insights, and underwriting optimization.
Summary Generated by Built In
Company:Guy Carpenter

Description:

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.

At Guy Carpenter, a Marsh business, you can be your best. We work on challenges that matter with colleagues who help bring out our best. Our uniquely collaborative environment will empower you to focus on your personal and professional success, learning from top specialists in the (re)insurance industry and leading you towards a rewarding and impactful career.

Guy Carpenter is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information about Guy Carpenter, visit guycarp.com, or follow us on LinkedIn and X.

Marsh is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, veteran status (including protected veterans), or any other characteristic protected by applicable law. If you have a need that requires accommodation, please let us know by contacting [email protected].

Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.

The applicable base salary range for this role is $105,000 to $192,500.

The base pay offered will be determined on factors such as experience, skills, training, location, certifications, education, and any applicable minimum wage requirements. Decisions will be determined on a case-by-case basis. In addition to the base salary, this position may be eligible for performance-based incentives.

We are excited to offer a competitive total rewards package which includes health and welfare benefits, tuition assistance, 401K savings and other retirement programs as well as employee assistance programs.

Skills Required

  • Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Engineering, or quantitative field
  • 3-6 years relevant experience in data science, advanced analytics, or quantitative modeling
  • Prior insurance industry experience at a carrier, reinsurer, broker, or analytics vendor
  • Experience working with insurance datasets and business problems (exposure, submission, pricing, claims)
  • Strong programming skills in Python and SQL
  • 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
  • Familiarity with R
  • Experience in underwriting analytics, portfolio analytics, or insurance performance analytics
  • Familiarity with GLMs, tree-based models, clustering, regularization, time series analysis, anomaly detection
  • 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
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The Company
HQ: New York, New York
9,026 Employees

What We Do

Oliver Wyman is a global leader in management consulting. With offices in more than 70 cities across 30 countries, Oliver Wyman combines deep industry knowledge with specialized expertise in strategy, operations, risk management, and organization transformation. The firm has more than 7,000 professionals around the world who work with clients to optimize their business, improve their operations and risk profile, and accelerate their organizational performance to seize the most attractive opportunities. Oliver Wyman is a business of Marsh McLennan [NYSE: MMC].

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