Data Scientist-Journeyman (Data[KGP2])

Air Combat Effectiveness Consulting•Lexington Park, MD

9h•$130,000 - $165,000

About The Position

The role of the journey level Data Scientist is to lead the analysis of programmatic and technical data as it is collected from various program and/or project offices. The Data Scientist will be responsible for working with a multi-network data management specialist to organize and analyze data against predetermined metrics. This person will be responsible for utilizing artificial intelligence (AI) models (CAPRA and or GenAI) to analyze data in support of the proposed gap analyses and business case analyses. Their skillset must include prompt engineering for AI models, data analysis, predictive modeling, and producing concise data summaries.

Requirements

Top Secret Clearance with SAP eligibility Required
Demonstrated experience delivering results in support of multi-level security projects.
Accredited B.S. degree relevant to data science or analytics.
Demonstrated experience in data analysis, predictive modeling, or applied statistics in a technical or programmatic environment.
Hands-on experience utilizing artificial intelligence and machine learning models, including prompt engineering for GenAI or similar tools.

Nice To Haves

Direct experience supporting ACAT-level programs.
Advanced degree in a relevant discipline.
Direct experience working with defense, aerospace, or government programmatic/technical data.
Experience with Python, R, SQL, or similar languages for data manipulation and analysis.
Experience automating data workflows and processes using scripting or workflow automation tools.

Responsibilities

Coordinate with program and project offices to gather, clean, and integrate technical and programmatic data from multiple sources.
Work closely with the multi-network data management specialist to structure, maintain, and ensure the integrity of large, complex datasets.
Analyze data against predetermined metrics to assess sustainment performance, readiness, and capability gaps.
Apply artificial intelligence and machine learning models (e.g., CAPRA, GenAI) to support gap analyses and business case analyses.
Develop and refine prompts to optimize the performance and relevance of AI-generated outputs for project needs.
Build and validate predictive models to forecast sustainment outcomes, readiness trends, and resource requirements.
Create clear, concise data visualizations and summaries to communicate findings to engineers, logisticians, and program managers.
Produce technical reports and actionable data summaries that support decision-making and project objectives.
Collaborate with cross-functional teams, including systems engineers, logisticians, and program managers, to ensure data-driven insights are actionable and aligned with project goals.