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Data Scientist with Security Clearance
Fuse Engineering LLCFort Meade, MD🇺🇸United StatesPosted 20 Jul 2026
Why This Role Stands Out
This hybrid role offers a fantastic opportunity to apply advanced NLP and machine learning techniques to critical security projects, accelerating your career growth within a reputable firm. You'll thrive here if you possess strong analytical and programming skills, a passion for data science, and the required security clearance, making this an exciting chance to make a significant impact.
Quick Overview
Work Type
Hybrid
Schedule
Employee
Level
Mid Senior
Job Description
Support for NLP project to accurately and automatically tokenize language data with spoken or written origins; develop automated solutions for the annotation of language data with parts of speech information, and improved existing models by scoring performance against human-generated annotations for speech and text. Clearance Required Top Secret SCI w/ Full Polygraph 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. Must have some combination (2 or more) of the following skill areas: Foundations: Mathematical, Computational, Statistical 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.
Skills
Linear
Machine Learning
NLP
Python
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