Data Scientist with Security Clearance

R-00170589 Description Leidos has a new and exciting opportunity for a Data Scientist in our National Security Sector's (NSS) Cyber & Analytics Business Area (CABA) located in McLean, VA. Our talented team is at the forefront in Security Engineering, Computer Network Operations (CNO), Mission Software, Analytical Methods and Modeling, Signals Intelligence (SIGINT), and Cryptographic Key Management. At Leidos, we offer competitive benefits, including Paid Time Off, 11 paid Holidays, 401K with a 6% company match and immediate vesting, Flexible Schedules, Discounted Stock Purchase Plans, Technical Upskilling, Education and Training Support, Parental Paid Leave, and much more. Join us and make a difference in National Security! Job Summary: We are looking for a Data Scientist with an entrepreneur’s mindset to support a fast-paced and growing data science team working on projects critical to National Security. Responsibilities Include: * Build statistical and machine learning models, conducting natural language processing (NLP), and developing automated solutions for complex business challenges.

* Construct and execute complex database search queries across multiple databases using SQL and API interfaces.

* Perform web scraping and apply various techniques for processing unstructured data.

* Leverage automation and machine learning to manage data, predict scenarios, and make data-driven recommendations.

* Learn and leverage technologies including AWS and Git. Basic Qualifications : * Must have TS/SCI with Polygraph.

* BS degree and 12 – 15 years relevant technical experience or Master’s with 10 – 13 years of relevant experience. May possess a Doctorate in technical domain.

* Experience with Python is required.

* Strong SQL skills and experience with relational databases and APIs for data extraction is required.

* Experience with supervised and unsupervised learning and natural language processing (NLP) is required. Familiarity with NLP libraries and tools (e.g., spaCy, Hugging Face, NLTK, or TensorFlow).

* Experience with frequentist statistics, probability, and predictive modeling, along with knowledge of computer science concepts, data architecture, and statistical methods.