Data Scientist SME

About the position

LTS is seeking a Data Scientist SME to support the Department of Commerce (DOC) Office of Cybersecurity. You will leverage advanced data analytics, machine learning, and statistical modeling techniques to identify and mitigate security threats while delivering actionable insights to strengthen the organization's cybersecurity posture. You will play a key role in supporting the Continuous Diagnostics and Mitigation (CDM) and Zero Trust Architecture (ZTA) programs by creating impactful dashboards and visualizations and integrating them into operational workflows. This role requires strong technical skills, cybersecurity awareness, and the ability to communicate complex data-driven insights to diverse stakeholders. This position is on-site in Washington D.C. The position is contingent on the award.

Responsibilities

• Develop and implement data-driven models and analytics to proactively detect, evaluate, and respond to cybersecurity threats.
• Provide technical support for current and future CDM/ZTA phases, including tool stack administration and reporting.
• Build visually compelling dashboards to support CDM reporting on Know Exploitable Vulnerabilities (KEVs) by Bureau.
• Ingest, normalize, and incorporate data feeds from multiple cybersecurity tools and platforms (Tenable Nessus, HCL BigFix, SailPoint, Okta, Axonius, Cloudflare, etc.) into centralized analytics environments.
• Create informative, actionable data visualizations to present cybersecurity insights to a wide range of audiences.
• Translate complex security and analytics concepts into clear, concise visual narratives for both technical and non-technical stakeholders.
• Collaborate with cybersecurity teams—including analysts, engineers, and fellow data scientists—to improve threat detection and risk analysis capabilities.
• Integrate data science and visualization solutions into existing security operations workflows.
• Present findings, insights, and recommendations through presentations, briefings, and formal documentation.
• Support the enhancement and evolution of enterprise cybersecurity initiatives by using analytics to guide decisions and improve outcomes.

Requirements

• Master's degree in Cybersecurity, Data Science, Computer Science, or a related field is required.
• A minimum of 5 years of experience integrating and analyzing data feeds from cybersecurity tools and external sources.
• Active TS/SCI clearance or TS with SSBI investigation (SCI eligible).
• CISSP, CISM, GSEC, CEH, GCTI, CompTIA Security+, CySA+, certification is preferred.
• Azure Data Scientist Associate, Certified Analytics Professional (CAP) or relevant cloud based/data science focused certification highly desirable.
• Experience in data science, cybersecurity analytics, or a related role, with proven ability to work with large and diverse data feeds.
• Proficiency in data analytics, statistical modeling, and machine learning techniques.
• Strong experience developing dashboards and visualizations using tools such as Tableau, Power BI, or similar.
• Proficiency in programming/scripting languages such as Python, R, or SQL for data analysis and model development.
• Strong understanding of cybersecurity principles, threat detection methodologies, and security event analysis.
• Familiarity with CDM and ZTA frameworks and tools, including Tenable Nessus, HCL BigFix, SailPoint, Okta, Axonius, and Cloudflare.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their associated security tools and services.
• Ability to interpret and communicate complex security data to both technical and non-technical audiences.
• Demonstrated ability to work in cross-functional teams and contribute to collaborative security solutions.
• Strong communication skills and experience delivering insights through documentation and presentations.
• Ability to engage collaboratively with multidisciplinary teams and high-level stakeholders.
• Excellent written and oral communication skills with the ability to tailor content to diverse audiences.
• Ability to work independently and collaboratively in a dynamic, fast-paced environment.