Founder & Construction Project Management Consultant — In Project LLC Construction Project / Program Management · Project Controls · Construction Data Analytics Lehi, Utah · English and Spanish
15+ years leading construction, design, and consulting work — multimillion-dollar portfolios, concurrent projects, multidisciplinary teams, budgets, schedules, procurement, contracts, risk, quality, change control, and stakeholder governance.
The recent work applies that background to measurement: project controls, dashboards, and predictive analytics that surface a problem while there is still time to act on it.
Three end-to-end case studies. Each publishes its data, SQL, Python, dashboards, and full report. Each ran the Ask · Prepare · Process · Analyze · Share · Act lifecycle.
Which project-controls metrics move first, early enough to intervene?
| Portfolio | 75 projects · $5.83B budget at completion |
| Result | 13.0% forecast overrun · weighted CPI 0.884 · 33.7-day average delay |
| Key finding | Contingency burn ratio has the strongest tested association with final overrun (Pearson r = 0.901), and is measurable from month one |
Where do change-order and RFI workflows create cost and schedule exposure?
| Portfolio | 90 projects · 3,318 RFIs · 1,119 change orders |
| Result | 12.21-day average RFI response · 45.7% on-time · $204.57M approved change value |
| Key finding | RFI response time and change approval cycle move together (Pearson r = 0.817) — they behave as one decision system, not two |
Can a material overrun or delay be predicted while intervention is still possible?
| Dataset | 2,362 projects · 40 predictors · time-based 2019–2025 split |
| Result | Cost-overrun ROC-AUC 0.899 · schedule-delay ROC-AUC 0.756 (2025 test period) |
| Key finding | Cost overrun is predictable early; schedule delay is harder and degrades over time — reported as a monitoring concern rather than smoothed away |
On the data. These three case studies use datasets generated for portfolio demonstration. They show method and decision support, and do not represent actual client performance or industry benchmarks. The predictive model is not authorized for production use; its output supports human review rather than replacing professional judgment.
Analytics Python · pandas · scikit-learn · SQL · SQLite · Excel Business intelligence Power BI · Tableau · executive dashboards · KPI design Project controls Earned Value Management · CPI / SPI / EAC · Primavera P6 · Microsoft Project · change and RFI workflow Governance Model cards · human review · drift monitoring · responsible AI
MS in Project Management (PUCMM / EOI) · BS in Business Administration (UAPA) PMP® · Google Data Analytics Professional Certificate · Google Project Management Specialization · Construction Management Specialization (Columbia University / Coursera) · Agile Hybrid Project (PMI)
Portfolio · LinkedIn · In Project · In Project AI · ndickson@inprojectmanagement.com
Open to construction project and program management, project controls, construction analytics, and consulting work — Utah-based and remote.