Lead Quantitative Analyst, Revenue Intelligence

Acrisure•Grand Rapids, MI

6d•Onsite

About The Position

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.

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.
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 for data manipulation, model development, and automation.
Proficiency in Power BI or comparable BI tools for building analytical dashboards and executive-ready visualizations.
Experience with Palantir or comparable enterprise data platforms for large-scale data integration and analysis.

Nice To Haves

Master's degree in Quantitative Finance, Financial Economics, Statistics, Econometrics, or a related field (e.g., MS in Mathematical Finance & Financial Technology or equivalent program).
Experience in insurance brokerage, financial services, or a multi-entity organization with complex revenue recognition dynamics.
Familiarity with insurance production systems such as Applied Epic or comparable commission and policy management platforms.
Experience supporting financial planning & analysis, strategic finance, or business intelligence functions with quantitative modeling outputs.
Experience with financial modeling and 3-statement synthesis
Experience managing complex financial projects across multiple business units and stakeholder groups

Responsibilities

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.
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.
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.