Job Specifications
About the Role
The Data Scientist/Machine Learning Engineer plays a critical role in advancing the organization’s cybersecurity analytics and automation capabilities. This position transforms complex cybersecurity and operational datasets into actionable insights that strengthen decision-making, streamline security processes, and accelerate digital modernization efforts.
Using tools such as Python, Anaconda, Selenium, SQL, Power BI, REST APIs, and modern ML libraries, this role will develop predictive models, automate manual workflows, and build high-impact analytics to support enterprise security operations, continuous monitoring, and risk management activities.
Key Responsibilities
Analytics & Visualization
Build and maintain dynamic dashboards, reports, and visualizations in Power BI and similar tools to help stakeholders interpret trends, risks, and operational performance.
Translate complex datasets into clear, actionable insights that guide cybersecurity prioritization and resource allocation.
Machine Learning & Data Science
Design, train, and deploy scalable ML models (classification, regression, anomaly detection, NLP, forecasting) that support proactive cybersecurity functions.
Develop AI/ML solutions for threat detection, anomaly scoring, behavioral analytics, and predictive security modeling, applying analytical rigor and creativity to solve real-world cybersecurity challenges.
Cybersecurity Automation & Engineering
Automate manual cybersecurity workflows - including control assessments, POA&M tracking, incident response tasks, risk scoring, and continuous monitoring reporting.
Use Selenium, Python scripting, and REST APIs to integrate and automate processes across tools such as ServiceNow, Splunk, Tenable, EDR platforms, and other enterprise systems.
Develop automated data ingestion pipelines and ETL processes to support real-time and batch analytics.
Threat Hunting & Security Analytics
Perform data-driven threat hunting identifying suspicious patterns by correlating log data from SIEMs (e.g., Splunk), EDR tools, firewalls, and network telemetry.
Build dashboards, detections, and automation workflows that enhance security situational awareness.
Data Management & Pipeline Development
Collect, clean, transform, and maintain large cybersecurity datasets in SQL/NoSQL environments.
Build scalable, high-performance data pipelines to ensure data quality, availability, and integrity.
Implement best practices for data governance, documentation, and reproducibility across analytics workflows.
Technical Skills
Programming: Strong proficiency in Python (preferred) or R for data retrieval, transformation, modeling, automation, and ETL workflows.
AI/ML Frameworks: Experience with Scikit-learn, TensorFlow, PyTorch, or equivalent frameworks for model development.
APIs & Automation: Ability to design, consume, and automate REST/GraphQL APIs; experience with Selenium for browser workflow automation.
Security Analytics: Hands-on experience performing SIEM analysis, building Splunk searches, and correlating EDR/network logs.
Data Visualization: Expertise with Power BI, including DAX and Power Query, to create interactive analytics products.
Data Engineering: Experience building automated pipelines, optimizing large datasets, and working with SQL and NoSQL databases.
Analytical & Professional Skills
Experience in statistical analysis, data mining, data preparation, predictive modeling, and performance evaluation.
Demonstrate a strong eagerness to learn cybersecurity concepts and continuously grow technical expertise in the field.
Understanding of the end-to-end ML lifecycle, including feature engineering, model training, deployment, and monitoring.
Ability to evaluate emerging AI and automation technologies and integrate them into cybersecurity workflows.
Strong problem-solving abilities, especially in dynamic, high-sensitivity security environments.
Education, Experience & Work Environment
Experience partnering across cybersecurity, engineering, IT operations, and program management teams.
Bachelor’s degree in Data Science, Computer Science, Statistics, Information Technology, Cybersecurity, or a related quantitative field (advanced degrees preferred).
2+ years of relevant experience in data science, analytics, ML engineering, or cybersecurity analytics.
Must be eligible for a Public Trust clearance.
Hybrid schedule—onsite 4 days per week in Washington, DC.