Senior Structures Data Scientist

Senior Structures Data Scientist

The Utah Department of Transportation (UDOT) is seeking a forward-thinking, highly motivated professional with exceptional communication skills and deep technical expertise to serve as our Senior Structures Data Scientist.

About the Role

In this role, you will act as the primary data steward for the Structures Division, spearheading data analytics, data modeling, and data management to optimize workflows, drive automation, and inform critical decisions.

Position Information

• Salary Range: $41.09 - $65.15
• FT/PT Status: Full Time
• Background Check: You must successfully pass a criminal history check.
• Schedule Code: B - Competitive Career Service (12 month probationary period)
• Location: Calvin Rampton Building – 4501 South 2700 West, Taylorsville, Utah
• Recruiter: Jill Barela I jbarela@utah.gov I 385-202-4484
• Application Deadline: 08/10/2026
  • *This job posting may close at any time after the minimum advertising period of three (3) business days has passed, should a sufficient applicant pool be received. Applicants are encouraged to apply as early as possible.

Key Responsibilities

• Advanced Analytics
  • Build interpretable, "white-box" predictive models to identify structural deterioration patterns. Use rigorous statistical methods to establish and validate causal relationships.
  • Integrate deterioration models with Lifecycle Cost Analysis (LCCA) to determine optimal intervention windows, maximizing ROI and infrastructure lifespan.
  • Assess how environmental, structural, and operational stresses impact degradation over time.
  • Align model outputs with FHWA NBI metrics and UDOT’s TAMP standards to justify asset management budgets and federal funding requests.
  • Translate complex findings, statistical anomalies, and technical limitations into clear, actionable business cases for various stakeholders.
• Data Engineering and Governance
  • Oversee and report on data quality. Champion improvement to data quality, consistency, and accuracy.
  • Architect and maintain automated ETL/ELT pipelines to ingest, clean, standardize and validate datasets from diverse sources.
  • Develop automated data quality and validation frameworks to detect anomalies in real-time, ensuring infrastructure datasets are reliable and compliant before entering production.
  • Establish standardized documentation framework to eliminate technical silos and preserve institutional continuity.
• Production, Deployment and Collaboration
  • Collaborate with cross-functional software and cloud engineering teams to automate the deployment, real-time monitoring, and scaling of models in production.
  • Act as the primary technical liaison, driving collaboration between the Structures Division, Division of Technology Services (DTS), cloud engineering, and external partners.
  • Translate validated algorithms into interactive dashboards, real-time APIs, and fully automated asset-scoring engines to deliver actionable, real-time insights for the division.
  • Research, investigate, and recommend technologies including web, mobile and other new or emerging technologies.
  • Meet with vendors on new and existing products, evaluate usefulness and cost of products and make recommendations.

Qualifications

• A Bachelor’s degree (or higher) from an accredited institution in Data Management, Business Administration, Math, Information Science, or another related field AND at least six (6) years of professional relevant experience in data analytics or other related field.
  • Note: Directly related professional experience may be substituted for the education requirement on a year-for-year basis (e.g., 10 years of total related experience if no degree is held).

Preferred Qualifications

• Structural Engineering/Bridge Related Experience: Background in bridge design, in-service inspection, load ratings, construction, and/or the long-term monitoring of concrete, steel or other materials used in bridge construction
• Data Visualization Tools: Experience using Microsoft Power BI, Looker Data Studio, or ArcGIS Online.
• AI and Machine Learning: Experience applying artificial intelligence (AI) and machine learning (ML) techniques to data management challenges.