Research Systems Engineer

Verisk Analytics•Boston, MA

10d•Hybrid

About The Position

This is a hybrid role that blends process leadership, cross‑functional collaboration, and technical contribution. You will work closely with project managers, researchers, and model development teams to embed best practices, improve operational efficiency, and ensure research outcomes are well‑documented, auditable, and scalable. If you enjoy understanding how work really happens—and making it better—this role offers meaningful impact and room to grow.

Requirements

Bachelor’s degree required (Math, Engineering, Science, or related field)
Experience in project management or supporting project‑based work
Experience contributing to process improvement initiatives
Curious, self‑directed learner with a genuine interest in how research work happens
Highly organized, adaptable, and comfortable with evolving requirements
Clear communicator who can work effectively with different roles and levels
Comfortable working across disciplines and reconciling competing priorities
Open to leveraging emerging tools—including generative AI—to improve efficiency and quality while maintaining compliance and rigor
Strong working knowledge of Microsoft Office, especially Excel, Word, and PowerPoint
Proficiency in Python or a similar scripting language
Experience building automation tools, dashboards, or data pipelines
Familiarity with version control systems (Git/GitHub preferred)

Nice To Haves

Master’s degree preferred
Experience with AI‑assisted coding tools is a plus

Responsibilities

Drive Research Excellence & Continuous Improvement
Identify opportunities to improve how research is planned, executed, documented, and delivered
Coach teams on best practices and support adoption through collaboration—not enforcement
Facilitate shared learning across teams to reduce duplication and improve consistency
Help embed a culture of trust, transparency, and measurable outcomes
Partner Across Research & Project Teams
Serve as a liaison between Research Operations and model development teams
Hold regular check‑ins with project managers to review progress, tooling usage, and documentation
Support long‑running research efforts (often multi-year) by ensuring processes stay aligned from start to finish
Expanded Technical Contribution
Designing and building automation solutions to support audits, documentation, or data management
Contributing to tooling improvements within Research Operations
Supporting model development teams with scripting, data workflows, or process‑enabling tools
Helping teams apply version control, reproducibility, and documentation standards