Summary Newly hired Data Scientists may be enrolled in the three-year Data Science Development Program (DSDP), in which they will both broaden and specialize their data science skills by taking courses and touring with a variety of mission offices (each for several months). You will have the opportunity to collaborate with NSA's experts in data science, related technical domains, and specialized subject areas while tackling NSA's highest priority mission challenges. Responsibilities 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. As a Data Scientist, your responsibilities may include: - Exploratory data analysis and exploratory model-fitting to reveal data features of interest - Machine-learned predictive modeling - Identifying and analyzing anomalous data (including metadata) - Lead or contribute to cross-functional teams to develop and implement Al (including generative AI) that can help solve some of our most challenging problems. - Apply modern engineering techniques to develop decision support software proto@types. - Propose and execute novel, cutting-edge research in Al-enhanced decision systems. - Designing and developing analytics and techniques for analysis - Analyzing data using mathematical and statistical methods - Implement ML pipeline and workflows - Leverage skills in modern data architecture, cloud engineering, data transformation, and management of structured and unstructured data sources. - Build continuous integration and delivery pipelines for ML applications. - Construct usable data sets from multiple sources to meet customer needs - 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 - Communicate with a team of data scientists, data engineers, AI engineers, ML-Ops engineers, and stakeholders. - Evaluating, documenting, and communicating research processes, analyses, and results to customers, peers, and leadership - Creating interpretable visualizations. - Work with mission owners to design, develop, and deploy new architectures for ML and automation applications such as ELT functions, HPC/compute infrastructure, AWS/Azure solutions, database solutions, and optimization of DevOps procedures. Requirements Conditions of Employment Qualifications The qualifications listed are the minimum acceptable to be considered for the position. Applicants who meet minimum qualifications may be asked to complete the Data Science Examination (DSE) evaluating their knowledge of statistics, mathematics, and computer science topics that pertain to data science work. Passing this examination is a requirement in order to be considered for selection into a data scientist position. Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines (i.e., behavioral, social, library, and life) may be considered if it includes a concentration of coursework (typically 5 or more courses) in advanced mathematics (typically 200 level or higher; such as calculus, differential equations, discrete mathematics, linear algebra, and calculus based statistics) and/or computer science (e.g., algorithms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper level math courses designated as elementary or basic do not count. Note: Degrees in related fields will be considered if accompanied by a Certificate in Data Science from an accredited college/university. ENTRY/DEVELOPMENTAL Entry is with a Bachelor's degree and no experience. An Associate's degree plus 2 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position. Relevant experience must be in one or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, programming, data science, advanced statistical analysis, artificial intelligence, computational science, software engineering, or data engineering. FULL PERFORMANCE Entry is with a Bachelor's degree plus 3 years of relevant experience or a Master's degree plus 1 year of relevant experience or a Doctoral degree and no experience. An Associate's degree plus 5 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position. Relevant experience must be in two or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, programming, data science, advanced statistical analysis, artificial intelligence, computational science, software engineering, or data engineering. Education Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines (i.e., behavioral, social, library, and life) may be considered if it includes a concentration of coursework (typically 5 or more courses) in advanced mathematics (typically 200 level or higher; such as calculus, differential equations, discrete mathematics, linear algebra, and calculus based statistics) and/or computer science (e.g., algorithms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper level math courses designated as elementary or basic do not count. Note: Degrees in related fields will be considered if accompanied by a Certificate in Data Science from an accredited college/university. Additional Information Pay: Salary offers are based on candidates' education level and years of experience relevant to the position and also take into account information provided by the hiring manager/organization regarding the work level for the position. Salary Range: $86,498 - $151,570 (Entry/Developmental, Full Performance) Salary range varies by location, work level, and relevant experience to the position. Training will be provided based on the selectee's needs and experience. Benefits: NSA offers a comprehensive benefits package. Work Schedule: This is a full-time position, Monday - Friday, with basic 8hr/day work requirement between 6:00 a.m. and 6:00 p.m. (flexible).
