Data Scientist - Experienced to Expert Level (Maryland)

Responsibilities

Data science at the National Security Agency (NSA) involves using mathematics, statistics, computer science, and domain-specific knowledge to analyze and interpret data. It includes roles such as AI Engineering, Data Engineering, ML Ops Engineering, and Human Perception and Cognition Engineering. Key responsibilities may include:

• Exploring data analysis and model fitting to reveal data features of interest.
• Using machine-learned predictive modeling.
• Constructing usable data sets from multiple sources to meet customer needs.
• Identifying and analyzing anomalous data (including metadata).
• Developing conceptual designs and models to address mission requirements.
• Developing qualitative and quantitative methods for characterizing datasets in various states.
• Performing analytic modeling, scripting, and/or programming.
• Working collaboratively and iteratively throughout the data-science lifecycle.
• Designing and developing analytics and techniques for analysis.
• Analyzing data using mathematical and statistical methods.
• Evaluating, documenting, and communicating research processes, analyses, and results to customers, peers, and leadership.
• Creating interpretable visualizations.

Qualifications

The qualifications listed are the minimum acceptable to be considered for the position. Applicants must pass the Data Science Examination (DSE) to be considered for selection into a data scientist position. Additionally, transcripts for each academic institution are required prior to being invited to interview with Agency data science professionals and should be submitted as part of the online application.

• Degrees in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Data Science, Operations Research, Quantitative/Computational Finance, Econometrics/Quantitative Economics, Computer Science, or Computer Engineering qualify without additional coursework or a Data Science certificate.
• Degrees in Engineering, Physical Sciences, Mathematical Biology/Bioinformatics, Life Sciences, Environmental Science, Data Analytics, or Information Science/Systems/Technology, must include either a Data Science certificate from an accredited college/university OR 5 or more courses in advanced mathematics (for example, calculus, differential equations, discrete mathematics, linear algebra, and calculus-based statistics) and/or advanced computer science (for example, algorithms, programming, data structures, data mining, artificial intelligence).
• Other degrees must be accompanied by a Data Science certificate from an accredited college/university, and must include a total of 5 or more courses in advanced mathematics AND advanced computer science. At least one course must be from advanced mathematics (for example, calculus, differential equations, discrete mathematics, linear algebra, and calculus-based statistics). At least one course must be from advanced computer science (for example, algorithms, programming, data structures, computer architecture, data mining, artificial intelligence).
• Experience must include both programming and one or more of the following: designing/implementing machine learning, data mining, statistical analysis, statistical consulting, artificial intelligence development, computational science, software engineering, technical writing, data visualization, or data engineering.
• Experience must also include formal or informal leadership.

Competencies

The ideal candidate has a desire for continual learning along with excellent analytical, problem-solving, communication (oral and written), and interpersonal skills who is:

• Accountable
• Proactive
• Detail oriented
• Able to solve complex problems
• Proficient with critical thinking and reasoning to make analytic determinations
• Effective at working in a collaborative team environment
• Able to bridge the gap with both technical and non-technical audiences
• Able to provide technical leadership in formal or informal roles

Knowledge, Skills, and Relevant Experience

The ideal candidate has knowledge, skills, and relevant experience in programming, formal or informal leadership, and one or more of the following: designing/implementing machine learning, data mining, statistical analysis, statistical consulting, artificial intelligence development, computational science, software engineering, technical writing, data visualization, or data engineering.