Logistics Management Institute

Data Scientist with an active TS/SCI with polygraph clearance

US-VA-Herndon
2 weeks ago
Job ID
2017-1507
# of Openings
1
Category
Data/Analytics

Overview

LMI currently has an opening for a Data Scientist supporting an Intelligence Community (IC) customer in the Reston / Herndon area.  The chosen candidate will work on a team of engineers and scientist developing highly complex programmatic and quantitative methods to find patterns and relationships in large data sets, utilizing complex mathematical, statistical or other data-driven problem solving analysis to identify significant intelligence issues or trends in business operations to senior internal and external customers.  Selected candidate will work under minimal technical guidance and be responsible for developing, testing, implementing, and maintaining complex enterprise-level software applications and/or specialized utility programs.


 

Responsibilities

Specific responsibilities include: 

Compile data to address the most complex intelligence issues or problems for internal/external customers. 

Provide accurate, timely, highly complex and sophisticated data analysis to support key IC customer, US national security, and foreign policy objectives; to advance IC customer business operations; and to shape Intelligence Community analysis and priorities. 

Conceive, prepare, and communicate a wide range of strategic, highly complex graphics, computational models/tools, or written/oral assessments to internal and external peers and customers (e.g., policymakers, IC components).

 

Qualifications

Requirements:

TS/SCI with polygraph needed

Minimum degree requested: Bachelor’s degree in a quantitative or technical field of study (e.g., statistics, mathematics, computer science, physical science, quantitative social science) and relevant internship or coursework experience. A MS or PhD in relevant degree program is desired. 

 

Desired background or special skills:

Demonstrated on-the-job experience with analytic methods and methodological tools in one or more of the following areas: applied mathematics (e.g., probability and statistics, formal modeling, computational social sciences), computer programming (e.g., programming languages, math/statistics packages, computer science, machine learning, scientific computing), or visualization (e.g., GIS/geospatial analysis, telemetry analysis). 

Experience with translating complex, technical findings into an easily understood narrative (i.e., tell a story with the data) in graphical, verbal, or written form. 

Experience with leveraging multiple data management tools to organize relevant information and make decisions. 

Experience in research design.

Experience with multitasking and changing focus quickly as demands change.

 

 

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