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The IDA Data Science Fellowship provides recent graduates a unique opportunity to develop and apply data science skills to important issues in national security. The fellowship is a project-based learning experience within a variety of research areas. In a collaborative team environment, fellows perform data manipulation and statistical, econometric, predictive, descriptive, and other quantitative analyses to help answer important sponsor-funded research questions as well as internal-funded business operations questions. In the course of research, fellows will apply advanced data science tools, possibly including machine learning, artificial intelligence, statistics, or various big data methodologies. Fellows should expect to learn while using their critical thinking, creativity, and analytic skills to contribute to interdisciplinary project teams.
What to Expect
Fellows will have opportunities to work on several research questions during their three-year terms. Example projects include:
Appraisal of Department of Defense investments in areas from human factors to autonomous systems, from materials science to nuclear weapons effects, and from social behaviors to quantum computing.
Analyze and research questions on military personnel, readiness and efficacy, and organizational efficiency topics.
Assessment of federal agency information and computing architectures such as large, distributed data sets and computational assets that support data science applications.
Application of data exploration, text analytics, forecasting, statistical inference, and simulation to areas of military personnel and workforce and acquisition of defense weapon systems.
Improve IDA’s data architecture to support efficient internal operations.
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 professional and academic meetings and conferences.
Who Should Apply?
This is a full time position and is only open to recent recipients of a bachelor’s or master’s degree. Individuals with degrees higher than a master’s degree are not eligible to apply.
Candidates must have at least a bachelor’s degree.
Candidate with 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
IDA is now accepting applications, which must be received no later than March 29, 2024. In addition to the application, applicants at later stages in the process will be required to submit the required documents as listed in the job announcement.
Code Sample, GitHub link or quantitative research writing sample —a code sample is preferred, but a writing sample from a research project or assignment demonstrating quantitative analytical ability is acceptable.
Writing Sample — a single page personal statement describing your interest in the Data Science Fellowship or a writing sample from a research project or assignment. (Not required if a writing sample was submitted for requirement 1.)
Transcripts — an unofficial transcript is acceptable for consideration, but an official transcript is needed before any offer of employment will be made.
Contact Information for 2 Academic/Professional References or 2 Letters of Academic Recommendation — references must include name, position, phone number, email address. Letters must be signed by the individual serving as the reference and delivered in PDF format.