Machine Learning Engineer USC

Machine Learning Engineer

Start: IMMED

Duration: 06 - 12 months + Extension

Location: Arlington, VA

Type: W2 only

***Active Secret Clearance Required***

Position Overview

The Machine Learning Engineer will develop and validate quantitative models that translate organizational workload drivers into defensible Full-Time Equivalent (FTE) requirements across military, civilian, and contractor workforces. This role will work closely with Data Scientists, AI Engineers, and functional stakeholders to build scalable workforce planning and forecasting solutions using advanced statistical and machine learning techniques.

Education

Bachelor s Degree Required

Advanced degree preferred in:

• Mathematics
• Statistics
• Econometrics
• Economics

Required Skills

• Machine Learning Modeling
• Econometrics
• Classical Regression Modeling
• Statistics
• Python

Preferred Skills

• Palantir
• Workforce Forecasting
• Workload Modeling
• AI and Data Testing

Day-to-Day Responsibilities

• Build and validate quantitative workforce planning and forecasting models.
• Translate organizational workload drivers into FTE requirements across military, civilian, and contractor populations.
• Collaborate with Senior Data Scientists and AI Engineers to explore, analyze, and prepare data.
• Perform feature engineering using Army personnel systems, including:
• IPPS-A
• DAPES
• TAADS-R
• Develop and calibrate regression models and plausibility banding logic.
• Integrate scenario-planning model parameters into front-end applications.
• Ensure model outputs are traceable, explainable, and defensible during client validation reviews.
• Support AI and workforce analytics initiatives through statistical analysis and model testing.
• Work with functional leads to validate assumptions, methodologies, and outputs.

Expected Deliverables

• Automated manpower requirement determination models.
• Data-driven workforce forecasting solutions.
• Quantitative workforce planning outputs supported by defensible statistical methodologies.
• Validated machine learning and regression-based forecasting models.
• Reporting and analytics outputs supporting manpower and resource planning decisions.

Ideal Candidate Profile

• Experience developing machine learning and statistical forecasting models.
• Strong background in regression analysis, econometrics, and workforce analytics.
• Proficiency in Python for data science and machine learning applications.
• Ability to explain complex modeling approaches to both technical and non-technical stakeholders.
• Experience working with large government or enterprise workforce datasets is highly preferred.