Data Scientist

• Creech AFB, NV (on-site)

• Integrate and manage data from multiple government systems and databases (e.g., Envision and other DAF sources) to create a unified data environment that supports analysis and decision-making across the Wing.
• Perform data collection, analysis, and interpretation across key mission areas such as manpower, readiness, medical status, aircrew training, and flying operations to deliver timely, accurate insights to leadership.
• Develop and maintain interactive dashboards and data visualizations that clearly communicate key performance indicators (KPIs), trends, and operational patterns to Wing, Group, and Squadron leadership.
• Build and apply predictive analytics models to identify resource gaps, forecast risks, and highlight at-risk areas, enabling proactive and data-driven decision-making.
• Provide technical support and training to Wing personnel, including the Wing Strategy Team and Commander’s Action Group, to enable effective use, customization, and basic development of data analytics tools and dashboards.
• Support data-driven decision-making processes by delivering actionable insights that improve resource allocation, training efficiency, and overall operational performance.
• Lead and support continuous process improvement initiatives to enhance data collection, integration, analysis, and reporting capabilities in alignment with evolving mission priorities.
• Collaborate with stakeholders across the Wing and subordinate units to improve data quality, streamline processes, and expand the use of analytics in operational and strategic planning.

• Bachelor’s Degree in computer science, operations research, or a comparable field.
• Minimum of 5 years of experience in professional software algorithm development and/or database management and visualization.
• Experience with software development lifecycle and use of associated tools.
• Proficient in Python, R, Matlab, Lua, or other data science- centric programming language.
• Mathematical knowledge of optimization, multi-variate calculus, probability & statistics, linear algebra, and numerical methods.
• Knowledge of supervised and unsupervised machine learning concepts, such as Artificial Neural Networks, SVMs, Random Forests, Gaussian Processes, and other techniques. Knowledge of common data visualization technologies, such as matplotlib, bokeh, d3, or matlab.
• Exceptional analytical skills and problem-solving skills.
• Willingness to learn new skills and technologies and exit “comfort zone”.
• Good organization, decision making, and verbal and written communication skills.
• High level of self-initiative and self-motivation with the ability to work under minimal supervision.
• Ability to work effectively in small team settings to solve complex problems.

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