Data Scientist (Integrated Prevention), US Army Reserve Command

Data Scientist (Integrated Prevention), US Army Reserve Command

Category : Alumni

This is a Direct Hire Authority (DHA) solicitation utilizing the DHA for Certain Personnel of the DoD Workforce to recruit and appoint qualified candidates to positions in the competitive service.

About the Position: This position is with the United States Army Reserve Command, Army Reserve G-1, Services and Support Division, Integrated Prevention Advisory Group (I-PAG) located at Fort Liberty, North Carolina.

Responsibilities: 

  • Serve as a senior Data Analyst for the U.S. Army Reserve (USAR) Integrated Prevention Program Initiatives with the mission to reduce or eliminate incidents of harmful behaviors through predictive analytics.
  • Lead development of process for data collection and analysis of prevention activities.
  • Conduct statistical analysis of enterprise-wide data from primary prevention initiatives.
  • Develop analytical plans to shape the organization’s data infrastructure, inclusive of data warehousing, reporting and analytics platforms.
  • Support the development of proposals and projects that align with primary prevention research and policy goals for data science research and analytic projects.
  • Participate with scientists and program managers both in various aspects of the study or survey design process.
  • Collaborate with statistical, data science, public health professionals in collection, linkage, processing, coding, classification, analysis of public health surveillance; research, admin data related to primary prevention program initiatives.
  • Summarize information from statistical analysis for wide dissemination and to facilitate information sharing.
  • Extract information, conduct analysis and develop reports or presentations using advanced analytical and visualization techniques.
  • Utilize analytics software, tools, emerging technology, various data sources to perform exploratory analyses on new/archival data sources to improve data reliability, efficiency, quality for analysis missions, organizational goals, future planning.

For more information and to apply, please click here


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