Business Data Lead - Insurance Operations

AmTrust Financial Services, Inc.

About The Position

The Business Data Lead will serve as a key contributor to our analytics function—bridging hands-on technical work with higher-level insight generation and cross‑functional collaboration. This role owns complex reporting and analytical projects, partners closely with business leaders, and helps improve the organization’s use of data, dashboards, and third‑party information. The ideal candidate is an experienced analyst who can independently drive projects from concept to delivery, mentor junior team members, and influence decision-making with data.

Requirements

Bachelor’s degree in Data Science, Computer Science, Economics, Business Statistics, Operational Research, or a related quantitative field.
4–6 years of experience in a data or business analytics role (less with Master's).
Experience in insurance industry.
Competent in Python or R for general purpose programming.
Advanced proficiency in SQL, confident and proven ability to retrieve and aggregate information across databases.
Intermediate proficiency with Excel, particularly pivot tables.
Demonstrated experience building dashboards or visualizations using Power BI, Tableau, ggplot2, or matplotlib.
Solid understanding of statistical inference and applied analytics.
Ability to deliver clear, concise insights to non-technical stakeholders.
Self-directed learner with the ability to manage multiple projects simultaneously.
Strong communication, collaboration, and presentation skills.

Nice To Haves

Familiarity with predictive modeling, ML, or advanced analytics techniques (forecasting, segmentation, scoring models).
Experience working with or evaluating third‑party datasets.
Exposure to DevOps, CI/CD, or agile project methodologies (Azure DevOps, Git, etc.).
Comfort with data architecture concepts and partnering with data engineering teams.

Responsibilities

Design, build, and enhance dashboards and analytical reports that improve visibility into pricing, underwriting workflow, submissions, and operational performance.
Partner with internal stakeholders to refine reporting needs and ensure outputs are actionable and easy to interpret.
Maintain and optimize existing reporting assets across SQL, Python/R, and BI tools.
Lead medium‑ to large‑scale analytical projects, owning the full lifecycle: requirements gathering, data extraction, transformation, analysis, visualization, and final recommendations.
Provide senior leadership with timely insights and scenario-driven analysis that supports decisions in pricing, underwriting, distribution, operations, or product.
Identify opportunities to automate manual processes, streamline workflows, and enhance analytical rigor across recurring reports.
Introduce and champion best practices in data management, visualization, and reproducible analysis.
Act as a liaison between technical teams and business functions, translating business needs into technical requirements and analytical findings into clear actions.
Support alignment between Business Analytics and Data Science teams by contributing to shared methodologies, metrics, and models.