Granite Construction · Safety Intern · Pacific Northwest · Summer 2026
The information was already in the reports. Nobody was reading across them.
I spent a summer as a safety intern at Granite Construction, one of the largest civil contractors in the U.S. My job was to help a regional safety team see what the field was already telling them: I went to jobsites, read incident narratives one at a time, built classification workflows with a human sign-off on every row, and turned the results into decisions leadership could act on.
Four projects, one through-line: get the right information to the right person before something goes wrong.
STCKY Luck vs. Success
Far more reports described a high-potential event than the tag had ever caught.
Leadership's headline safety ratio was built from a handful of tagged events. I read every report in the region instead, with a strict decision tree and a person approving every call, and rebuilt the number from evidence.
Read the case studyWork Zone Intrusion Classifier
Turning messy near-miss narratives into a spec a development partner could build against.
"Car went around flagger." "Truck drove through job site." Real intrusions were buried in a general log with no shared definition. I wrote one, built the pipeline, and reviewed every row.
Read the case studyJob Site Management Hub
One place to find every active site, who to call, and air-quality alerts by text.
Site addresses, meeting points, contacts and PPE rules lived in emails and people's heads. I built a Power Apps hub fed by three automated flows, then handed it to the enterprise team to ship.
Read the case studyLight-Vehicle Incidents & Field Work
Moving a supervisor conversation from driver blame to exposure management.
Every light-vehicle incident in the region this year, presented at a supervisor SHEQ meeting, plus the jobsite inspections and observations that kept every data project honest.
Read the case studyHow I worked
Research first. Then a definition, a machine, and a person who signs off.
Go to the site
Every data project started on a jobsite. Inspections, observations and a SHEQ assessment showed me what a "near miss" actually looks like before I tried to classify one.
Write the definition down
Most of the disagreement in safety data comes from undefined words. I wrote one explicit definition per project and applied it the same way to every record.
Let a person decide
The model reads and explains; a reviewer approves, excludes or escalates. Nothing reached leadership without a human decision behind it and a source anyone could check.
Tools I used
- Power Automate
- Copilot Studio
- AI Builder
- Power Apps
- Dataverse
- SharePoint
- Excel
- HCSS incident data
- PowerPoint
- Power Automate
- Copilot Studio
- AI Builder
- Power Apps
- Dataverse
- SharePoint
- Excel
- HCSS incident data
- PowerPoint
Why this belongs in a UX research portfolio
None of these projects started with a tool. They started with people who needed a clearer picture: a safety manager defending a number, a supervisor deciding where trucks park, a crew member trying to find the meeting point on a site they'd never visited. The method was the same one I use in UX research: observe, define, gather evidence, and present it so someone can act.
Let's talk
Have a research problem? I'll read every report.
Open to UX research internships and entry-level roles. Interviews, usability tests, survey design, and the patient kind of document analysis that finds what dashboards miss.