Process Improvement Analyst Intern

Genworth
Raleigh, North Carolina, United StatesPosted Sep 14, 2026

About the role

At Enact, we understand that there’s no place like home. That’s why we bring our deep expertise, insightful offerings, and extra mile service to work every day to help lenders put more people in homes and keep them there. We’re looking for a Process Improvement Analyst Intern in Raleigh, NC to join us in fulfilling our mission, while utilizing our values of excellence, improvement, and connection. In addition to the multiple weekly events attended by all interns, you’ll work alongside industry experts for a 12-week internship beginning May 17, 2027. In this role, you will support the expansion of our internal Operations Copilot by contributing to evaluation, monitoring, and continuous improvement processes.

THE PROGRAM

Enact’s Internship Program provides an excellent opportunity for students to gain hands-on experience in achieving business results while developing technical and professional skills. The internship program is a critical component of Enact’s talent pipeline as we look to interns to provide cutting edge ideas and skills that will drive business success. Enact invests time, energy, and resources to create a worthwhile experience for interns. Interns work on meaningful business projects, but beyond that, they will network directly with leaders throughout the company (all the way up to the CEO), attend teambuilding events with their internship cohort, connect with the community through volunteer opportunities (both on-site and off-site), and attend professional development workshops. It’s a unique internship experience that helps interns develop a network of peers and mentors who act as a source of constant engagement and guidance during the internship.

LOCATION

Enact Headquarters, Raleigh, NC – Hybrid Schedule

YOUR RESPONSIBILITIES

Design month-to-month sampling aligned to company statistical methods and implement reproducible cohorts. Define and curate a “golden dataset” covering happy paths, edge cases, and synthetic augmentation to evaluate consisten

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