Senior Actuarial Analyst, Predictive Modeling

AmTrust Financial Services, Inc.•New York, NY

22h•$60,000 - $118,000

About The Position

Looking for a driven, inventive, and highly technical actuarial candidate to build and maintain tools that support modeling and actuarial workflows across multiple lines of business. This person will sit within the Actuarial Predictive Modeling tools team and focus on developing and maintaining applications, automations, and infrastructure that make reviewing and monitoring predictive models more effective and transparent. Just as importantly, this role requires someone with actuarial judgment who can understand the business and modeling reasons behind the tools being built, help identify where new tools or enhancements would add value, and ensure those solutions are grounded in real actuarial needs. You will design both backend and frontend portions of these tools with an eye toward scalability, maintainability, and utility, working with colleagues across actuarial, underwriting, and data science. AmTrust is an innovative, customer-centric leader in commercial insurance. We're looking for someone who can navigate complex data and codebases, contribute to multiple concurrent projects, and proactively identify opportunities to improve how actuarial work gets done. The right candidate will not only build tools, but also help interpret their output, surface meaningful insights and limitations, and communicate what the results mean to both technical and non-technical audiences.

Requirements

Bachelor's degree in Actuarial Science, Mathematics, Computer Science, Statistics, or related field
3+ years of actuarial experience, actively pursuing ACAS or FCAS
3+ years of hands-on experience in data exploration, manipulation, and visualization with either R or Python, with at least working familiarity with the other language
Comfort with reading and writing complex SQL queries
Experience with Git for version control and collaborative development
Dashboarding experience in Power BI (including DAX), Shiny, Streamlit, or similar frameworks
Strong grasp of statistical methods applied to insurance data, including the ability to identify and articulate data and modeling issues
Experience identifying opportunities for workflow enhancements and successfully implementing changes or new tools to address them
Ability to build trust and rapport across teams and create a collaborative, effective workplace
Excellent problem-solving skills and attention to detail
Demonstrated ability to experiment with and learn new technologies

Nice To Haves

Experience with Posit Connect and Posit Workbench
Experience creating packages in R or Python
Experience optimizing code including R, Python, and SQL queries
End-to-end experience building predictive models, including some involvement in implementation and deployment
Familiarity with model interpretability tools/packages (SHAP, tree-based interpretation, etc.)
Experience with data modeling in Power BI / Microsoft Fabric

Responsibilities

Build and maintain actuarial tools and applications — including interactive Shiny and Streamlit apps, Power BI dashboards, and data pipelines — that support model review, model interpretation, data validation, reporting, and decision-making across lines of business
Develop process automations to streamline recurring workflows, such as automated data checks for reporting, and monitoring tools for data drift and model drift
Understand and refactor existing codebases to improve performance, maintainability, and documentation; manage multiple tools and projects concurrently
Collaborate with data science, actuarial, and business teams to translate analytical needs into well-designed tools and dashboards, and proactively identify opportunities for new tools or enhancements
Evaluate and adopt emerging technologies, including AI/Copilot capabilities
Communicate results, tool capabilities, and tool output to technical and non-technical stakeholders, including explaining what results mean, where limitations exist, and how outputs should inform decisions
Foster strong coding and data operations best practices, including version control (Git), code review, and change management processes