Data Scientist - Entry to Expert Level (MD, HI, CO)

Responsibilities

At NSA Data Science is a broad field, and a team effort, spanning all the expertise needed to derive value from data. As a Data Scientist, you will uses elements of mathematics, statistics, computer science, and application-specific knowledge to gather, make, and communicate principled conclusions from data. 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 challenges. Responsibilities 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

Job Summary

Depending on the skill-sets currently in demand, newly hired Data Scientists may be assigned to a mission office, or alternatively 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). In either case you will work with NSA experts in data science, related technical domains, and specialized subject areas. You will have opportunities to participate in internal technical roundtables, and to attend technical conferences with experts from industry and academia.

Qualifications

Applicants will 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 at a local testing site is a requirement in order to be considered for selection into a data scientist position. Upon passing the examination, applicants will be evaluated for the minimum qualifications outlined in this ad. Transcripts will be requested prior to being invited to interview with Agency data science professionals. The qualifications listed below are the minimum acceptable to be considered for the 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. 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. 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. 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. 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. An Associate's degree plus 8 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position. 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 relevant experience. An Associate's degree plus 11 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position.

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 Knowledge, skills, and relevant experience in one or more of the following is required: - Designing and implementing machine learning - Data mining - Advanced analytical algorithms - Programming - Data science - Advanced statistical analysis - Artificial Intelligence - Computational science - Software engineering - Data engineering