LMI is seeking a knowledgeable and dynamic Social Network Analysis (SNA) Instructor to join our team on a part-time/hourly basis to lead engaging and comprehensive training sessions on fundamental concepts in SNA in support of a U.S. Federal Government Agency working with classified media. The course, titled Introduction to Social Network Analysis, is a two-day program held every other month. The instructor will guide students through hands-on exercises, contextualize SNA techniques for diverse datasets, and showcase best practices and tools in this field. This role offers an exciting opportunity to shape the knowledge and application of SNA concepts for learners eager to unlock deeper insights from networks and data systems.

• Teach graph networks, including nodes, edges, weights, and directionality.
• Cover core metrics such as degree, betweenness, closeness, eigenvector centrality, and combinations of metrics.
• Explore neighborhoods, steps, and k-cores, providing practical applications.
• Guide students in understanding scalability challenges and applying SNA to data sets of varying sizes.
• Introduce and explain community detection methods and techniques for identifying patterns within networks.
• Teach pattern analysis, helping students identify trends and connections in networks.
• Provide guidance on contextualizing SNA results to extract meaningful insights from diverse data types.
• Administer practical exercises, guiding students through related hands-on activities.
• Demonstrate the effective use of provided SNA tools and software.
• Highlight best practices and recommend strategies to address common challenges in SNA.
• Emphasize the broad applicability of SNA across various fields, including social sciences, business, healthcare, cybersecurity, and beyond.
• Foster a deep understanding of methodological concepts and approaches to network analysis.
• Provide constructive feedback to enhance student learning and encourage critical thinking.
• Prepare and submit written post-course feedback to help refine course curriculum.

Required

• Proven experience in teaching or applying Social Network Analysis concepts to real-world problems.
• Strong knowledge of SNA metrics, graph theory, community detection, and network pattern analysis.
• Hands-on experience working with SNA tools and software.
• Familiarity with handling scalability issues and best practices in network analysis.
• Exceptional communication skills with the ability to explain complex concepts in a clear and engaging manner.
• A passion for guiding and supporting students to achieve their learning goals.
• Bachelor’s Degree in applicable discipline.
• TS/SCI with polygraph.

Desired

• Experience working in customer analytic environment.
• Proficiency with client-specific tools.