Data Scientist Development Program: From Analyst to Expert

National Security Agency; Fort Meade (MD); On-site; USD 90,000 - 130,000; Full time

Job summary

Data science at NSA uses elements of mathematics, statistics, computer science and application-specific knowledge to gather, make, and communicate principled conclusions from data. It encompasses AI engineering, data engineering, ML Ops engineering, and human perception and cognition engineering, in addition to traditional applications of data science.

Responsibilities may include: Exploring data analysis and model-fitting to reveal data features of interest, using machine-learned predictive

Qualifications

• Data science uses elements of mathematics, statistics, computer science and application-specific knowledge to gather, make, and communicate principled conclusions from data.
• It encompasses AI engineering, data engineering, ML Ops engineering, and human perception and cognition engineering, in addition to traditional applications of data science.

Responsibilities

• 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 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

Job description

Data science at NSA uses elements of mathematics, statistics, computer science and application-specific knowledge to gather, make, and communicate principled conclusions from data. It encompasses AI engineering, data engineering, ML Ops engineering, and human perception and cognition engineering, in addition to traditional applications of data science.

Responsibilities may include: Exploring data analysis and model-fitting to reveal data features of interest, using machine-learned predictive