> Markdown version of [/jobs/ext/3957720-data-scientist-3](https://www.wearedevelopers.com/jobs/ext/3957720-data-scientist-3). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist 3 - **Company:** Sentar, Inc. - **Location:** Fort Meade, MD, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, Big Data, Computer Programming, Data Cleansing, Data Mining, Data Structures, Statistical Hypothesis Testing, Internet Security, Python (Programming Language), Machine Learning, Software Engineering, Data Processing, Scripting, Information Technology, Data Management, Tools for Reporting - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/data-scientist-3-fort-george-g-meade-md--c4dc2dc7-9601-4ded-9e78-c0659074fe78 ## About the Role Data Science, Systems Engineering, Internet Security, Talent Management, Data Science, Computer Science, Python Programming/Scripting Language, Mathematics, Data Processing, Data Management, We are seeking a Data Scientist proficient in Python, GM, and the customer toolset. This role involves leveraging your computer science expertise to develop and sustain analytics tools. The Level 3 Data Scientist shall possess the following capabilities: * Employ must have some combination (2 or more) of the following skill areas: * Foundations: Mathematical, Computational, Statistical * Data Processing: Data management and curation, data description and visualization, workflow and reproducibility * Modeling, Inference, and Prediction: Data modeling and assessment, domain-specific consideration. * Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge. * Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in National Agency data holdings. * Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. * Effectively communicate complex technical information to non-technical audiences., Clearance Level: TS/SCI with active poly, * Bachelor's Degree with 10 years of relevant experience, Associates degree with 12 years of experience may be considered for individuals with in-depth experience that is clearly related to the position. * Bachelor's?Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence). College-level requirement, or upper-level math courses designated as elementary or basic do not count. * Broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. Experience: * Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least on high level language (e.g. Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.