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Marsh McLennan
Marsh McLennan
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Marsh McLennan is a global professional services firm working across risk, strategy, and people-related advisory services. Its businesses include Marsh, which provides insurance brokerage and risk management; Guy Carpenter, focused on reinsurance and capital strategies; Mercer, serving clients in health, wealth, and career consulting; and Oliver Wyman, specializing in strategy, economics, and brand consulting. With a workforce of more than 85,000 employees worldwide, Marsh McLennan brings together expertise across finance, consulting, marketing, and related professional services. The company’s hiring landscape spans roles supporting client advisory, risk analysis, business strategy, consulting, and operational teams across its four businesses.
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Senior Product Analytics Specialist, Property Risk — Marsh McLennan

Develop property risk and loss analytics for Marsh McLennan, covering catastrophe and non-catastrophe scenarios. Turn insurance data and model findings into practical guidance for business and portfolio decisions.

CHSwitzerlandHybridFull-timeSeniorEnglish RequiredData Analyst
PythonSQLTableau

Description

  • Work with brokers, client leaders, product teams, and analytics partners to define risk and renewal challenges and establish analytical objectives
  • Prepare, standardize, and enhance data from exposure, policy, claims, pricing, engineering, and external hazard or vendor models
  • Develop, evaluate, and validate Property Risk Quantification models for catastrophe and non-catastrophe applications
  • Use pricing, actuarial, and risk-based methods to inform loss modeling, portfolio analysis, and business decisions
  • Create prototypes for dashboards, reports, and analytical outputs that make model results actionable
  • Partner with product, engineering, and data teams to productionize workflows, strengthen data foundations, and scale analytics capabilities
  • Record model assumptions, methods, data lineage, and related documentation in accordance with governance standards
  • Help stakeholders interpret model outputs clearly and transparently
  • Adapt effectively within a fast-moving incubation environment as priorities, use cases, and scope change
  • Structure emerging analytics challenges and drive them toward progress

Requirements

  • Bring approximately 5–8 years of relevant experience in analytics, actuarial work, catastrophe modeling, pricing, or insurance risk analysis
  • Hold a bachelor’s degree in actuarial science, mathematics, statistics, engineering, economics, data science, or another quantitative field, or offer equivalent practical experience
  • Demonstrate a strong grounding in statistical modeling, loss modeling, and model validation, preferably in insurance, pricing, or actuarial settings
  • Have experience with catastrophe modeling, property risk analytics, or comparable risk quantification methods
  • Have worked with insurance data and related analytical tools
  • Experience with RMS, AIR/Verisk, or comparable modeling platforms is advantageous
  • Use Python and/or SQL confidently for data preparation, analytics, and modeling
  • Have experience building reproducible analytical workflows
  • Know BI and visualization tools such as Power BI, Tableau, or equivalent platforms
  • A master’s degree in a quantitative discipline or progress toward an actuarial qualification is advantageous
  • Bring experience from insurance, reinsurance, broking, or risk advisory, especially property catastrophe and non-catastrophe analytics
  • Translate technical findings into useful business recommendations and communicate effectively with varied stakeholders
  • Work comfortably in ambiguous, rapidly changing, and lightly structured environments
  • Demonstrate a product-oriented approach, shaping methods, refining problem definitions, and adjusting as priorities develop

Benefits

  • Hybrid working arrangements with flexibility for remote work
  • Opportunities for collaboration, connection, and professional development through office-based work
  • A diverse, inclusive, and flexible workplace

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