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# Data Analyst - DoT LiDAR

- **Company:** Precision Inc.
- **Location:** United States (Remote available)
- **Salary:** $104,000.0
- **Contract:** Temporary contract
- **Skills:** ArcGIS (Software), Spatial Databases, Global Positioning Systems (GPS), GPS EXchange Format, Python (Programming Language), PostGIS, Quantum GIS (QGIS), SQL Databases, Geospatial Data Abstraction Library (GDAL), Lidar, Software Version Control
- **Published:** September 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=66e5c545c11b1ad7

## About the Role

Python with GeoPandas, Shapely, or GDAL, and SQL with PostGIS or an equivalent spatial database

 - Experience checking real sensor or GPS data for quality: gaps, drift, outliers, clock offsets

 - Comfortable computing geometry from coordinates: distances, headings, curve fitting, radius of curvature

 - Reproducible work habits: scripts and notebooks under version control, results a second person can rerun

 - Clear written English. Your QC notes go to a federal research center

Preferred

 - Transportation or roadway geometry work: AASHTO Green Book concepts, superelevation, design speed

 - Experience with vehicle telematics, naturalistic driving data, or smartphone sensor logs (GPS, accelerometer, gyroscope)

 - Point-cloud experience: PDAL, Open3D, CloudCompare, or LiDAR-derived surfaces

 - Prior work on a federally funded research project, or a state department of transportation

## Description

Nine drivers will record a month of their daily trips on their own phones through two Iowa road sites, an innovative interchange and a winding two-lane segment. A vehicle-mounted 3D LiDAR survey covers the same roads. You are the person who makes sure the data that reaches the Government is complete, correct, and reproducible.

What you would do

 - Run quality checks on incoming trip data as it arrives: completeness, GPS and sensor sanity, timestamp continuity, coverage of each site per driver

 - Keep the quality-control log that goes to the Government with the dataset: what was checked, what was rejected, why

 - Build the horizontal curve inventory for each site from the trip and LiDAR data: curve start and end points, radius, superelevation, and speed through each curve

 - Compare posted speed limits against design-supported safe speed at each curve, working with our licensed Professional Engineer who reviews the findings

 - Produce the figures and tables for the Task 3 and Task 4 reports and the final report, reproducible from code, * Describe one dataset where you found and fixed a quality problem in sensor or GPS data. What was wrong and how did you catch it?
 * Which of these have you used in the last two years: GeoPandas, GDAL, PostGIS, PDAL, QGIS, ArcGIS?
 * Have you computed curve radius or superelevation from coordinate or survey data? Where?
 * Are you willing to be named on the project team and credited in a report delivered to the Federal Highway Administration?


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