Data Scientist, Level 3

Independent Software•Annapolis Junction, MD

About The Position

As a Data Scientist Level 3 at Independent Software, you will play a mission-critical role in applying advanced data science, artificial intelligence, and machine learning techniques to complex national security challenges. In this role, you will support cyber operations by assessing large language models (LLMs) and advanced analytics to determine their effectiveness and applicability for Computer Network Exploitation (CNE) capability integration and operational utilization. You will work closely with the CNO community, supporting cyber mission tools and capabilities where familiarity with CNE concepts, workflows, and tooling is strongly preferred. Your work will focus on transforming complex, large-scale, and often unstructured data into actionable intelligence through statistical modeling, machine learning, and data-driven discovery. Leveraging strong Python skills, workflow automation, data normalization, and visualization techniques—particularly within Jupyter Notebooks—you will develop analytical tools, assess model performance, and communicate results to both technical and non-technical stakeholders. This role is ideal for a mathematically and statistically grounded professional who is inquisitive, discovery-oriented, and motivated to apply AI/ML in direct support of mission outcomes.

Requirements

Proficiency in Python, including experience developing analytics in Jupyter Notebooks.
Strong background in mathematics and statistics, with applied experience in machine learning and statistical modeling.
Hands-on experience working with large-scale, complex, and unstructured datasets.
Experience with data normalization, transformation, and visualization techniques.
Familiarity with AI/ML frameworks and assessing model performance and applicability.
Experience supporting or analyzing cyber operations, with familiarity in Computer Network Exploitation (CNE) concepts strongly preferred.
Experience with exploratory data analysis, hypothesis testing, and inference modeling.
Workflow automation experience, including optimization of data pipelines and ensuring reproducible analytics.
Strong problem-solving skills with an inquisitive, discovery-focused mindset.
Must possess an active TS SCI with appropriate Polygraph to be considered for this role
Master’s degree with 8 plus years of relevant experience
Bachelor’s degree with 10 plus years of relevant experience
Associate degree with 12 plus years of relevant experience
Mathematics, Applied Mathematics, Statistics, Machine Learning, Data Science, Operations Research, or Computer Science
Related fields such as Computer Information Systems, Engineering, or Physical Sciences with a strong computational focus
Other scientific disciplines may qualify if accompanied by 5 or more upper level courses in mathematics 300 level or higher and or computer science
Broader degrees may be accepted if supported by a Data Science Certificate from an accredited institution

Nice To Haves

Experience supporting or analyzing cyber operations, with familiarity in Computer Network Exploitation (CNE) concepts strongly preferred.

Responsibilities

Apply data science expertise across at least two of the following domains: Foundations: Mathematical, statistical, and computational theory and application
Data Processing: Data curation, normalization, transformation, visualization, reproducibility, and pipeline automation
Modeling and Prediction: Statistical modeling, inference, forecasting, machine learning, and domain-specific analytics
Assess large language models (LLMs) to determine effectiveness and applicability for CNE capability integration and operational use.
Support the CNO community by analyzing data related to cyber tools, techniques, and capabilities.
Perform data normalization, statistical analysis, and visualization to support operational decision-making.
Draw conclusions from large, complex datasets using a combination of mathematical, statistical, and domain knowledge.
Develop and apply analytic models and quantitative techniques tailored to unstructured or incomplete datasets with national security constraints.
Translate mission and analytical requirements into technical data specifications and analytical workflows.
Guide others in interpreting analytical results and model outputs.
Effectively communicate complex technical concepts to diverse audiences, including non-technical stakeholders.