Data Scientist Development Program (DSDP) - Entry to Expert (MD, CO)

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