Role purpose. Technical lead responsible for integrating discipline estimates and translating detailed cost analysis into project-level cost intelligence. Experience guideline. Typically requires 10-15 years of progressively relevant experience in the applicable professional discipline. Equivalent demonstrated capability, complexity of prior assignments, professional judgment, and leadership may be considered in level placement. Primary Responsibilities

• Lead multidisciplinary estimate assignments and coordinate G/A/S, Electrical, Mechanical, and Site Civil inputs.
• Independently prepare and review discipline estimates and interpret evolving design information.
• Establish estimating approach, basis, coding structure, and technical work plan.
• Review estimates for completeness, consistency, scope alignment, and reasonableness.
• Lead estimate reconciliations, benchmarking, and major cost-driver analysis.
• Direct senior-level QA/QC and resolve cross-discipline scope interfaces.
• Mentor Analysts and Senior Analysts and assign technical work.
• Coordinate directly with owners, designers, PM/CM firms, and contractors.
• Develop actionable recommendations based on cost intelligence.
• Ensure consistent use of PCS data, technology, and documentation standards.

Core Competencies Technical leadership; integration across workstreams; senior QA/QC; complex problem solving; mentoring; client facilitation; decision-oriented recommendations. Education & Credentials Bachelor’s degree in construction management, engineering, architecture, quantity surveying, building science, or a related field is preferred; equivalent directly relevant experience may be considered. Relevant professional credentials such as AACE, ASPE, or discipline-specific certifications are valued based on role and market requirements. AI & Technology Expectations Uses approved PCS technology, analytics, and AI-enabled tools responsibly; validates outputs against source information and professional judgment; protects client and company data; documents material assumptions; and escalates uncertain or unsupported outputs rather than treating AI-generated results as authoritative.