Data Scientist - Experienced to Expert Level (Maryland)

Full Job Description

Newly hired Data Scientists will be assigned to a mission office by aligning their data science skills with mission needs.

Due to the challenging and evolving nature of NSA data science problems, Data Scientists are expected to continue broadening and deepening their expertise throughout their career.

NSA data science is collaborative and you are expected to work with NSA experts in data science, related technical domains, and specialized subject areas.

SENIOR Entry is with an Associate's degree plus 8 years of relevant experience, or 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.

Note that different degree fields have different requirements as described below.

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.

EXPERT Entry is with an Associate's degree plus 11 years of relevant experience, or 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 relevant experience.

Note that different degree fields have different requirements as described below.

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. Major Duties:

Data science at the National Security Agency (NSA) is a multi-disciplinary field that uses elements of mathematics, statistics, computer science, and application-specific knowledge to gather, make, and communicate principled conclusions from data.

Data Science is a broad field and a team effort, spanning all the expertise needed to derive value from data.

It encompasses AI Engineering, Data Engineering, ML Ops Engineering, and Human Perception and Cognition Engineering in addition to the traditional applications of data science.

Data science is present in every aspect of the mission.

NSA Data Scientists tackle challenging real-world problems leveraging big data, high-performance computing, machine learning, and a breadth of other methodologies.

We are looking for critical thinkers, problem solvers, and motivated individuals who are enthusiastic about data and believe that answers to hard questions lie in the yet-to-be-told story of diverse, complicated data sets.

You will employ your mathematical science, computer science, and quantitative analysis skills to develop solutions to complex data problems and take full advantage of NSA's capabilities to tackle the highest priority foreign intelligence and cybersecurity challenges.

Responsibilities may include: - Exploring data analysis and model-fitting to reveal data features of interest - Using the machine-learned predictive modeling - Constructing usable data sets from multiple sources to meet customer needs - Identifying and analyzing anomalous data (including metadata) - Developing conceptual design 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