2021 Data Science Fellowship
The Institute for Defense Analyses (IDA) is a not-for-profit corporation that operates three Federally Funded Research Centers (FFRDCs). IDA’s Systems and Analyses Center is located in Alexandria, Virginia and is comprised of eight research divisions providing objective analyses of national security issues, particularly those requiring scientific and technical expertise, and conducts related research on other national challenges.
The IDA Data Science Fellowship provides recent graduates a unique opportunity to develop and apply data science skills to important issues in national security. Data Science Fellows will investigate the ways machine learning, artificial intelligence, statistics, and big data methodologies contribute to national security and gain experience developing advanced data science tools They will use their critical thinking and analytic skills to work on challenging real-world national security issues. In a collaborative team environment, Fellows perform data manipulation and statistical, econometric, predictive, descriptive, and other quantitative analyses to answer national security-related research questions. Fellows should expect to learn while creatively contributing to interdisciplinary project teams.
Fellows will have an opportunity to work research questions in areas such as:
- Appraisal of current DoD investments in a broad range of areas: from human factors to autonomous systems, from materials science to nuclear weapons effects, from social behaviors to quantum computing;
- Analyze and research questions on DoD personnel, military readiness and efficacy, and organizational efficiency topics.
- Assessment of federal agency information and computing architectures that support data science applications (i.e., large, distributed data sets and computational assets);
- Application of data exploration, text analytics, forecasting, statistical inference, simulation to areas of military personnel, manpower, and acquisition of DOD weapon systems.
Over the course of the three-year program, fellowship experiences will include:
- Involvement in workshops and discussions on relevant topics
- Mentorship from members of the IDA research staff
- Training on specific analytical methods and tools
- Attendance and presentation at select professional society meetings
This is a full time position and is only open to recent recipients of a bachelor’s or master’s degree. Candidates with degrees higher than a master’s degree will not be considered.
Candidate with recent Bachelor’s degree or Masters’ degree in economics, statistics, operations research, mathematics, physics, computer science, data science, or related disciplines with a strong foundation in statistics and/or applied mathematics are encouraged to apply.
Candidates must demonstrate experience with one or more programming language or statistical software used in research (e.g., Python, R, Julia, Stata, MATLAB, C, Java, etc.)
Candidates must demonstrate strong written and oral communication skills. Ideal candidates are able to contribute to and support team efforts.
Additional preferred skills include:
- Training and/or experience in quantitative or qualitative information collection, data normalization, and text analytics
- Experience as a research assistant in an academic or policy research setting
- Experience or coursework in Bayesian statistics, machine learning, predictive analytics, and/or geospatial analyses
- Experience with GPU, high-throughput, and/or distributed computing
U.S. citizenship is required.
In addition to the application, applicants at later stages in the process will be required to submit the following documents:
Personal Statement— explain in 1,000 words or less why you want to work in the area of data science and analytics in defense and national security and how your background/experience will contribute to IDA’s work
Writing Sample—5,000 word maximum
Transcripts - unofficial transcript is acceptable for consideration, but an official transcript is needed before any offer of employment will be made
Two Letters of Academic Recommendation—letters must be signed by the individual rendering the reference and delivered as PDF files.
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