Lead Quantitative Analyst, Revenue Intelligence

Acrisure; Grand Rapids (MI); On-site; USD 120,000 - 180,000; Full time

Job summary

Acrisure's Revenue & Commission Insights function is building a first-in-kind revenue intelligence capability—connecting policy-level and transaction-level 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 approaches to complex revenue dynamics in insurance brokerage.

Qualifications

• Deep expertise in econometric and statistical modeling methods with interpretable drivers.
• Strong financial acumen and ability to link operational drivers to financial results.
• Comfort using AI-assisted development tools to accelerate workflows.
• Excellent communication, translating quantitative findings for non-technical audiences.

Responsibilities

• Build and maintain time-series forecasting models for GAAP revenue incorporating drivers like retention, new business growth, and pricing trends.
• Develop econometric models to quantify drivers of revenue across segments, lines of business, and channels.
• Design scenario analysis tools enabling real-time revenue forecasting under varying macroeconomic and operational assumptions.
• Connect operational data to GAAP results, bridging insurance production metrics and recognized revenue.
• Build Power BI dashboards translating model outputs into clear visuals for senior leadership.

Skills

Econometric modeling

Time-series analysis

Regression analysis

Causal inference

Financial acumen

Communication

AI tools

Tools

Python

SQL

Palantir

Claude Code

Power BI

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.

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 lead