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NSA's Mathematics Research Group conducts world-class mathematical research with the objective of developing new and innovative techniques and technologies to support our Signals Intelligence and Cybersecurity missions, as well as the broader Intelligence Community (IC). We are actively seeking mathematicians with experience in machine learning security and generative AI to join our Statistics and Machine Learning Research Office. The office focuses on fundamental mathematics research in the applications of machine learning and statistical analysis, as well as a wide range of other technical areas to include cryptography, machine learning security, generative AI, network defense, and graph algorithms.

Responsibilities may include:

- Analyze problems and determine procedures required to solve technical problems;
- Create computer algorithms, data models, and protocols;
- Identify new applications of known techniques;
- Analyze data, algorithms, and communication protocols using mathematical/statistical methods;
- Develop and apply mathematical or computational methods and lines of reasoning;
- Design, develop and debug software solutions;
- Create and maintain documentation on research processes, analyses and/or the results;
- Write logical and accurate technical reports to communicate ideas;
- Effectively instruct, mentor, and support the professional development of colleagues in the areas of technical expertise.

Conditions of employment

• All applicants and employees are subject to random drug testing in accordance with Executive Order 12564. Employment is contingent upon successful completion of a security background investigation and polygraph.

Qualifications

The qualifications listed are the minimum acceptable to be considered for the position.

Degree must be in Mathematics, Physics, Engineering, Data Science, Computer Science, Statistics, or a related STEM field. Degree must include at least 24 semester credit hours (or 36 credit hours from universities on a quarter system) in advanced mathematics courses.

Relevant experience must be in one or more of the following: the design, development, use, and evaluation of mathematics models, methods, or techniques (for example, algorithm development) to study issues and solve problems. Experience may also include, network engineering, computer science, physics, software engineering, electrical engineering. Leadership experience can count for up to half the experience requirement.

SENIOR
Entry is with a Bachelor's degree plus 6 years of relevant experience, or a Master's degree plus 4 years of relevant experience, or a Doctoral degree plus 2 years of relevant experience.

EXPERT
Entry is with a Bachelor's degree plus 9 years of relevant experience, or a Master's degree plus 7 years of relevant experience, or a Doctoral degree plus 5 years of experience.

How you will be evaluated

You will be evaluated for this job based on how well you meet the qualifications above.

Qualified applicants will have a strong technical background in a computational science discipline (e.g., Mathematics, Statistics, Data or Computer Science) and research experience in mathematical analysis of large data sets. Experience in operational areas is a plus.

Exceptional candidates will have experience applying machine learning methods, including but not limited to a subset of deep learning, reinforcement learning, ensemble methods, and large scale graph analytics. Significant programming experience, especially working with large data sets (e.g., Python, Tensorflow, R, Java, C/C++, and/or other data processing frameworks) is preferred.

The ideal candidate is someone with excellent problem-solving, communication, and interpersonal skills, who possesses a range of knowledge and experience with:

- Applying principles and methods of linear algebra (e.g., vector spaces, matrices, matrix manipulations) to solve complex problems;
- Applying the mathematical principles, combinatorial methods or elicitation techniques to determine or calculate the likelihood of outcomes;
- Quantifying the likelihood of an event's occurrence;
- The scientific principles, methods, and processes used to conduct research studies (e.g., study design, data collection and analysis, and reporting results);
- Applying data-analytic techniques to analyze, visualize, and summarize sample data from populations;
- Drawing inferences regarding populations based on results from sample data.
- Concepts and procedures for applying algorithm design techniques (e.g., data structures, dynamic programming, backtracking, heuristics, and modeling) to design correct, efficient, and implementable algorithms for real-world problems;
- Debugging and testing software programs;
- Using best programming practices (e.g., appropriate coding standards, algorithm efficiencies, coding documentation);
- Using principles, techniques, procedures, and tools that facilitate the development of software applications;
- Using software and computer languages and skills (e.g., writing code, debugging/testing programs, fixing syntax, correcting logic errors, using abstract data types) to develop programs that meet technical requirements;