Lead Quantitative Analyst, Revenue Intelligence

About this role

Job Description Lead   Quantitative Analyst, Revenue Intelligence  

About Acrisure   

A global fintech leader, Acrisure empowers millions of ambitious businesses and individuals with the right solutions to grow boldly forward. Bringing   cutting-edge   technology and top-tier human support together, we connect clients with customized solutions across a range of insurance, reinsurance, payroll, benefits, cybersecurity, mortgage services – and more .    

In the last twelve years, Acrisure has grown in revenue from $38 million to almost   $5 billion   and employs over 19,000 colleagues in more than 20 countries. Acrisure was built on entrepreneurial spirit. Prioritizing leadership, accountability, and collaboration, we equip our teams to work at the highest levels possible.  

Job Summary  

Acrisure's Revenue & Commission Insights function is building a first-in-kind revenue intelligence capability — connecting operational insurance data to GAAP financial results and translating that connection into forward-looking models that drive strategic decisions. We are seeking a   Lead   Quantitative Analyst to sit at the intersection of financial analysis and quantitative modeling, bringing rigorous, economically grounded approaches to some of the most complex revenue dynamics in the insurance brokerage industry.  

Reporting to the Head of Revenue & Commission Insights, this is a senior individual contributor role. The ideal candidate brings deep   expertise   in econometric and statistical modeling,   a strong foundation   in financial concepts, and the technical fluency to build production-quality analytical tools. This is not a black-box machine learning role — we need models that are transparent, attributable to economic drivers, and defensible to senior finance and business leadership  

Responsibilities  

Revenue Modeling & Forecasting  

• Build and   maintain   time-series forecasting models for GAAP revenue, incorporating economic drivers including retention rates, new business growth, and pricing trends.  

• Develop econometric models to   identify   and quantify the drivers of revenue performance across client segments, lines of business, and distribution channels — producing outputs that are interpretable and attributable to specific business factors.  

• Design and   maintain   scenario analysis tools that enable real-time revenue forecasting under varying macroeconomic and operational assumptions, supporting executive decision-making and financial planning cycles.  

• Connect operational data (policy-level and transaction-level) to GAAP financial results, building the analytical bridge between insurance production metrics and recognized revenue.  

Growth & Performance Analytics  

• Analyze drivers of new business growth and client retention rates by line of business, client segment, and geography — translating statistical findings into actionable insights for senior leaders.  

• Build performance analytics frameworks for client advisor productivity,   identifying   leading indicators of revenue growth and attrition risk at the advisor and book-of-business level.  

• Conduct client segment performance analysis,   applying   clustering and segmentation techniques to   identify   high-value growth opportunities and inform resource allocation decisions.  

• Partner with FP&A leadership and Strategic Finance Business Partners to integrate quantitative insights into the annual planning and forecasting process.  

Tool Development & Data Infrastructure  

• Develop and   maintain   analytical models and tools using Python, SQL, and Palantir, with a focus on scalability, reproducibility, and ease of use by finance and business stakeholders.  

• Leverage Claude Code and AI-assisted development techniques to accelerate model building, code quality, and documentation — bringing modern development practices into the finance analytics function.  

• Partner with Data & Analytics and Financial Systems teams to define data requirements, resolve data quality issues, and expand access to policy-level and commission data sources including Applied Epic.  

• Build Power BI dashboards and reporting layers that translate complex model outputs into clear, decision-ready visualizations for senior finance and business leadership.  

Requirements  

• Deep   expertise   in econometric and statistical modeling methods — including time-series analysis, regression modeling, and causal inference — with a strong preference for interpretable, driver-based approaches over black-box prediction.  

• Strong financial acumen, with the ability to connect operational and economic drivers to financial statement outcomes; prior experience in financial services, insurance, or a similarly complex revenue environment is a plus.  

• Comfort working with AI-assisted development tools, including Claude Code, to accelerate analytical workflows and improve code quality.  

• Excellent communication skills — able to translate complex quantitative findings into clear narratives and business recommendations for non-technical audiences, including senior finance and business leaders.  

• Strong project ownership mindset; able to manage multiple modeling workstreams independently, prioritize effectively, and deliver high-quality work in a fast-paced environment.  

• Strong stakeholder management with the ability to navigate across business   units to   identify   and understand business needs and translate into   budgeting and forecasting processes.   

Required   Education and Experience  

• Bachelor's degree in Quantitative Finance , Economics, Statistics, Mathematics, or a closely related quantitative field;   Master's   degree   strongly   preferred.  

• 5–10 years of experience in quantitative modeling, financial analysis, or economic research, with a demonstrated   track record   of building econometric or statistical models in a business context.  

• Advanced   proficiency   in Python and SQL f

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