About Us: At Everly, our vision is to change the paradigm of insurance. We know life insurance can be confusing and complex. We know that most people purchase life insurance at a milestone moment and file it away for the future. We want to change how people think about, buy, and use life insurance by being a partner in life’s journey. We are focused on making things that matter, making meaningful connections, and making things work WAY better. Our goal is to provide radically transparent, consumer-first insurance solutions. We are looking for people with a continuous improvement mindset. Those who think the opposite of “This is how we’ve always done it”. We want people on our team who are always asking “How can we do this better?” Our company values are a guide to our behavior and help inform our decisions. We want people who embody those values of breaking down barriers, owning the outcome, embracing the journey, and sparking joy. This is a remote position based in the US with occasional travel. About the role: As a Modeling Lead, you will work closely with the Head of Modeling to design, build, and operate Everly’s actuarial modeling ecosystem and the supporting actuarial/finance data foundation. This is a highly hands‑on role spanning actuarial models, enterprise data design, and end‑to‑end data pipelines that power pricing, reserving, reporting, and analytics. You will also help leverage AI‑enabled tooling to accelerate the development and evolution of Everly’s in‑house actuarial modeling platform—improving development velocity, validation workflows, documentation, and insight generation while maintaining strong actuarial controls and governance. This position is ideal for a senior actuarial modeler who is comfortable operating at the intersection of actuarial science, software engineering, and modern data platforms. What You’ll Do: 1. Actuarial Modeling & Execution - Build, enhance, and maintain actuarial models for pricing, reserving, projections, and financial analytics. - Translate modeling standards and approaches set by the Head of Modeling into robust, production‑ready implementations. - Implement, test, and maintain actuarial assumptions, experience studies, and stress/scenario analyses. - Support recurring and ad‑hoc model runs across development, validation, and production environments. - Refactor and optimize models for performance, transparency, and scalability. 2. Enterprise Data Model & Modern Data Pipeline Support - Help design and maintain the enterprise actuarial and finance data model, ensuring consistency across pricing, valuation, financial reporting, and analytics. - Partner with Data Engineering and Technology teams to support end‑to‑end data pipelines (ingestion → transformation → storage → consumption) that make actuarial outputs reliable and reusable. - Support data validation, reconciliation, and controls between source data, model inputs/outputs, and downstream reporting. - Contribute to a modern data lake / lakehouse approach where appropriate, and support use cases across platforms such as Snowflake and open table formats. 3. Modeling Infrastructure, Controls & Governance - Contribute to shared modeling frameworks, libraries, and reusable components. - Support model versioning, documentation, testing, and change management processes. - Assist with internal model reviews, validation efforts, audit requests, and regulatory support. - Identify model and data risks, limitations, and dependencies, and communicate them clearly. 4. AI‑Enabled Modeling & Platform Evolution - Partner with the Head of Modeling to apply AI and automation to improve speed and quality of model development and enhancement. - Use AI‑enabled tooling to support activities such as development acceleration, test generation, documentation drafting, model/result validation workflows, and scenario exploration—while keeping human actuarial judgment in control. - Evaluate new approaches pragmatically, prioritizing auditability, reproducibility, and measurable impact. 5. Cross‑Functional Partnership - Work closely with Finance, Product, Data, Technology, and Operations teams to ensure actuarial models and data outputs are actionable and consumable. - Translate actuarial and financial results into clear insights for non‑actuarial stakeholders. - Support strategic initiatives including new product development, reinsurance analysis, and platform evolution. Skills and Experience: - Bachelor’s degree in Actuarial Science, Mathematics, Finance, Statistics, or a related field. - Progress toward, or attainment of, ASA or FSA (or equivalent designation). - 6–10+ years of experience in actuarial modeling within life insurance and/or annuities. - Strong hands‑on experience in pricing and/or reserving models. - Strong quantitative development skills with proficiency in one or more of: Python, R, C++, Rust, Java. - Experience working with large datasets and structured analytical/modeling workflows. - Strong analytical skills with high attention to detail, controls, and data integrity. What Sets You Up for Success at Everly: - You enjoy building and improving systems, not just maintaining them. - You thrive in early‑stage environments where structure is evolving. - You value clean interfaces between models, data, and reporting. - You’re curious about how AI can augment actuarial work, and grounded enough to apply it thoughtfully in a regulated environment. Compensation and Benefits: At Everly, we celebrate our diverse backgrounds and support our differences, and we are dedicated to equal employment opportunities regardless of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other legally protected status. We’re also committed to adding new perspectives to our team and invite applications from people of all walks of life. We understand that experience comes in many forms, so if you believe you’re close to what we’re looking for, please consider applying. Everly offers a competitive salary and as a full-time employee you are eligible for our robust benefits package including: - Employees are eligible for an annual incentive bonus designed to reward for performance. - The salary range for this job in most geographic locations in the US is $161,000 to $176,000 - Candidates hired to work in other locations will be subject to the pay range associated with that location and will be reflected in the candidate’s offer letter. - Flexible paid time off for PTO, plus paid holidays, days of Significance, and a Volunteer Day - Paid parental leave eligible after 3 months of service - Medical, Dental & Vision Insurance - 401k with company match - Profit Sharing & Savings Plan - Short-term and long-term disability insurance - Flexible spending account - Life insurance - Educational Assistance - Associate Assistance Programs and more! Visit the career section to apply and submit your resume. EOE

• Contribute to production readiness through monitoring, incident response support, and operational runbooks to meet agreed service levels • Conduct root-cause analysis of production issues that lead to adverse model outputs • Design, build, and optimize marketing mix models (MMM), attribution models, and other econometric or causal models using marketing data • Select and implement appropriate machine learning models for time-series forecasting, supply-chain optimization, and marketing performance use cases • Contribute well-tested, maintainable code to existing object-oriented Python codebases • Analyze raw, large-scale datasets to deliver meaningful impact for enterprise clients while maintaining scientific rigor • Engineer data pipelines and data products that enable stakeholders to make informed, data-driven marketing and business decisions • Communicate analytical insights in a clear, intuitive, and visually compelling manner for marketing, product, and executive audiences • Create highly visual and interactive dashboards using Tableau, Power BI, Looker, or custom web applications • Conduct deep-dive analyses and design KPIs to measure marketing effectiveness, ROI, incrementality, and business growth • Engineer data infrastructure, libraries, and APIs supporting BI, ML, and marketing analytics pipelines • Architect cloud data platform components that enable scalable analytics and modeling solutions • Build and manage project timelines, dependencies, and risks • Gather stakeholder requirements and perform technical due diligence to design pragmatic, data-driven marketing and business solutions

• Contribute to production readiness through monitoring, incident response support, and operational runbooks to meet agreed service levels • Conduct root-cause analysis of production issues that lead to adverse model outputs • Design, build, and optimize marketing mix models (MMM), attribution models, and other econometric or causal models using marketing data • Select and implement appropriate machine learning models for time-series forecasting, supply-chain optimization, and marketing performance use cases • Contribute well-tested, maintainable code to existing object-oriented Python codebases • Analyze raw, large-scale datasets to deliver meaningful impact for enterprise clients while maintaining scientific rigor • Engineer data pipelines and data products that enable stakeholders to make informed, data-driven marketing and business decisions • Communicate analytical insights in a clear, intuitive, and visually compelling manner for marketing, product, and executive audiences • Create highly visual and interactive dashboards using Tableau, Power BI, Looker, or custom web applications • Conduct deep-dive analyses and design KPIs to measure marketing effectiveness, ROI, incrementality, and business growth • Engineer data infrastructure, libraries, and APIs supporting BI, ML, and marketing analytics pipelines • Architect cloud data platform components that enable scalable analytics and modeling solutions • Build and manage project timelines, dependencies, and risks • Gather stakeholder requirements and perform technical due diligence to design pragmatic, data-driven marketing and business solutions

• Contribute to production readiness through monitoring, incident response support, and operational runbooks to meet agreed service levels • Conduct root-cause analysis of production issues that lead to adverse model outputs • Design, build, and optimize marketing mix models (MMM), attribution models, and other econometric or causal models using marketing data • Select and implement appropriate machine learning models for time-series forecasting, supply-chain optimization, and marketing performance use cases • Contribute well-tested, maintainable code to existing object-oriented Python codebases • Analyze raw, large-scale datasets to deliver meaningful impact for enterprise clients while maintaining scientific rigor • Engineer data pipelines and data products that enable stakeholders to make informed, data-driven marketing and business decisions • Communicate analytical insights in a clear, intuitive, and visually compelling manner for marketing, product, and executive audiences • Create highly visual and interactive dashboards using Tableau, Power BI, Looker, or custom web applications • Conduct deep-dive analyses and design KPIs to measure marketing effectiveness, ROI, incrementality, and business growth • Engineer data infrastructure, libraries, and APIs supporting BI, ML, and marketing analytics pipelines • Architect cloud data platform components that enable scalable analytics and modeling solutions • Build and manage project timelines, dependencies, and risks • Gather stakeholder requirements and perform technical due diligence to design pragmatic, data-driven marketing and business solutions