Cost and Analysis Engineer

Required Qualifications

• Minimum of 5+ years of experience with a Bachelor's degree, 3+ years with a Master's degree, or a PhD in Engineering, Finance, Mathematics, Statistics, Computer Science, or related field
• Direct software engineering experience with a focus on quantitative analysis or financial modeling
• Strong Python proficiency; experience with numerical computing libraries (Num Py, pandas, Sci Py)
• Understanding of earned value management principles and metrics
• Experience with schedule analysis concepts (critical path method, float, schedule compression)
• Familiarity with statistical forecasting and time series analysis
• Experience with data validation and quality assurance for analytical systems
• Strong mathematical and analytical reasoning skills
• US Citizenship with the ability to obtain/maintain a Secret clearance

• Direct experience with EVM systems or defense cost reporting
• Experience with Monte Carlo simulation (Crystal Ball, @Risk, or custom implementations)
• Familiarity with IMS data formats (Microsoft Project XML, Primavera XER/XML)
• Knowledge of IPMDAR/IPMR data elements and reporting requirements
• Experience with financial modeling or cost estimation

Peraton Labs is seeking a quantitative engineer to build the analytical engines that power cost and schedule analysis capabilities within an advanced program management platform. This role translates earned value management principles, schedule analysis methodologies, and financial forecasting techniques into automated, scalable software components that deliver actionable insights to program managers and oversight personnel.

• Develop EVM analytics engines: CPI, SPI, TCPI, EAC, VAC, and IEAC calculations with trend analysis and forecasting
• Build IMS parsing and analysis capabilities: critical path identification, float analysis, schedule health metrics, and baseline comparison
• Implement budget vs. actuals variance analysis with drill-down by WBS element, control account, and organizational structure
• Develop forecasting models that incorporate both quantitative EVM trends and qualitative signals from unstructured analysis
• Build schedule risk analysis (SRA) capabilities including Monte Carlo simulation integration
• Implement data validation and anomaly detection for cost and schedule data inputs
• Create reusable analytical components that can be invoked through the platform's automation framework
• Collaborate with PM Domain Analysts to validate analytical accuracy against established standards