AI Threat Analyst - Insider Risk

Sierra Nevada Corporation•Herndon, VA

1d•$136,114 - $187,157

About The Position

As an AI Threat Analyst III, you will support the Insider Risk Program and champion the Security Program’s business operations and AI‑driven initiatives. You will collaborate with key program stakeholders, using cyber tools to surface insider‑threat trends, advance insider‑risk awareness, and enable proactive defensive and offensive security capabilities. Our team is responsible for government affairs, international strategy, international business, and trade and regulatory compliance. We provide strategic guidance across the organization, helping to find alignment and compliance between SNC and the national security priorities of the U.S. government.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, or a related Intelligence field.
5+ years of experience in data science or a related role.
Higher level relevant degree may substitute for experience.
Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 9 years of related experience is required.
Experience with cyber security tools, applications and/or software such as Security Information and Event Management (SIEM)/ User and Entity Behavior Analytics (UEBA) platforms.
Proven ability to analyze large and unstructured data sets to discover active insider threats, uncover insider risk indicators and/or emerging AI threat trends, and identify anomalies indicative of malicious insider activities.
Proficiency in programming languages such as Python or R.
Experience with data visualization tools (e.g., Tableau, Power BI).
Strong analytical and problem-solving skills.
Data-oriented personality.
Ability to display strong analytical and communication skills.
Applied statistics skills, such as distributions, statistical testing, regression, etc.
Knowledge of machine learning techniques and algorithms, Large Language Models (LLMs/Generative AI).
Experience with common data science toolkits.
Proficiency in using query languages such as SQL.
Good scripting and programming skills( i.e.; Python)
Self-directed and outcome oriented - willing to ensure quality output, identify gaps in process, propose solutions, and drive initiatives to completion with minimal oversight.
An active Top Secret U.S. Security Clearance is required.
Ability to manipulate and analyze large datasets.
Ability to work on a computer for extended periods.
Must be able to present complex data findings in an understandable manner.
Must be able to lift up to 10 lbs occasionally.

Nice To Haves

A continuous learner across the AI threat landscape and new training opportunities to build AI fluency on both offense and defense Security.
Adaptive problem solver - comfortable operating in evolving threat climates, translating novel AI-enabled risks into practical detections, policy, risk mitigations proactively.
Active Insider Threat, Cyber or AI certifications and/or membership across industry recognized standards (Ex. ISC2, CMU ITPM, SANS, etc.).
Familiarity with cloud platforms (e.g., AWS, Azure).
Experience with data visualization tools, such as Power BI, Tableu, etc.
Experience with AWS Data Warehousing: AWS RDS (PostgreSQL), Redshift.
Experience with NoSQL databases, such as MongoDB, Cassandra, HBase, Amazon DynamoDB.

Responsibilities

Be a trusted partner for the Security team and all Insider Risk program stakeholders - willing to collaborate cross-functionally to foster a proactive, holistic insider threat program.
Drive the development and deployment of complex data science, insider threat and AI security solutions.
Collaborate with program leadership and senior stakeholders to shape data strategy and define project goals.
Implement advanced AI solutions for both offense and defense security operations.
Ensure data quality, security, and governance.
Mentor and guide junior and mid-level threat analyst.
Lead the creation of detailed reports and visualizations to communicate insights and recommendations.
Implement advanced statistical models and machine learning algorithms.
Continuously enhance program processes, tools, and methodologies.
Stay abreast of industry threat trends and advancements in technology.
Perform data cleaning, preparation, and transformation to ensure the quality and integrity of data used for analysis.