• Define analytical strategies to address mission-critical challenges and engage stakeholders, including Army leadership, to align solutions with strategic objectives. 

• Manage Agile data activities. 

• Lead the end-to-end development of machine learning models, including problem framing, data preparation, feature engineering, model selection, and evaluation. 

• Deploy machine learning models into production systems, ensuring scalability, reliability, and integration with existing software platforms. 

• Collaborate with software engineers and DevOps teams to implement automated pipelines for model deployment and monitoring. 

• Conduct advanced data integration, modeling, and analysis to extract actionable insights and drive decision-making. 

• Mentor junior team members and contribute to establishing best practices for AI and machine learning development. 

• Active DoD Secret Clearance required. 

• Bachelors degree in computer science, engineering, or related field required. Graduate degree preferred.

• 10+ years relevant experience. 

• Expertise in:  

• Logistics and supply chain data 

• Developing and deploying machine learning models in production environments. 

• Big data technologies and distributed computing (e.g., Spark, Hadoop). 

• Data visualization 

• Programming (Python, Spark, SQL) 

• Advanced machine learning and analytics techniques (e.g., deep learning, natural language processing, statistical models). 

• Software development workflows and version control systems (e.g., Git, CI/CD pipelines). 

• Exceptional communication skills to convey technical findings to non-technical audiences. 

Preferred: 

• Palantir Foundry platform experience 

• Familiarity with secure data handling in DoD IT environments. 

• Knowledge of US Army Logistics data and mission requirements. 

Travel: Minimal (< 10%) 

Targeted Salary Range: $104,000-$183,000

Disclaimer:

The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.