Description
Leidos has a new and exciting opportunity for a Data Analyst 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 seeking a self-motivated Data Analyst to provide technical and analytic support to a small, high-impact team. This is a developmental position with opportunities for career growth along a Data Science track.

BASIC QUALIFICATIONS:

• TS/SCI clearance with Polygraph is required.
• Experience with Python is required.
• Requires Bachelor’s degree and 8 – 12 years of relevant technical experience or Masters with 6 – 10 years of relevant technical experience. May possess a Doctorate in technical domain.
• Experience with statistical modeling: Poisson Distribution (or count-based models), time-series or panel date, specifically with spatial-temporal dimensions, and Predictive and Regression modeling.
• Experience/ knowledge of computer science concepts and data architecture.
• Experience with all-source intelligence, including SIGINT, and/or HUMINT collection reports.
• Experience with producing Finished Intelligence or structured threat assessments.
  At Leidos, the opportunities are boundless. We challenge our staff with interesting assignments that allow them to thrive professionally and personally. For us, helping you grow your career is good business. We look forward to learning more about you – apply today.

• Collaborate with data scientists and intelligence analysts to develop statistical models that support threat detection, forecasting, and operational decision-making.
• Build and deploy predictive, regression, and count-based models (e.g., Poisson), including time-series and spatial-temporal analyses, to forecast the onset, severity, and spread of events.
• Translate complex intelligence questions into statistically sound models and defensible insights, clearly communicated to both technical and non-technical stakeholders.
• Analyze crime, terrorism, and national security datasets to identify signals and patterns, producing structured threat assessments and finished intelligence.
• Automate analytical workflows and machine learning models in Python, leveraging cloud platforms (e.g., AWS) and version control tools (e.g., Git) in a secure, collaborative environment.