DATA SCIENTIST (CYBER/CLOUD)

Responsibilities

• Support the formulation, design, and execution of data-driven projects focused on cybersecurity risk prioritization, vulnerability analysis, exposure trends, anomaly detection, and automated risk reporting.
• Develop, fine-tune, and assist in deploying AI/ML models and LLM-based solutions (e.g., for natural language understanding, summarization, and insight generation from unstructured sources such as SBOMs and technical documentation).
• Help integrate model outputs into data pipelines, dashboards, and visualization tools to deliver clear insights for technical and non-technical stakeholders.
• Collaborate with data analysts, software developers, and cross-functional teams to embed predictive analytics and LLM capabilities into existing platforms.
• Clean, collate, and preprocess complex datasets from multiple sources; assess and improve data quality.
• Apply statistical methods, machine learning algorithms, and LLM techniques to extract actionable insights under guidance.
• Perform model validation, performance monitoring, and testing.
• Optimize models for accuracy, scalability, and usability.
• Produce and present reports, summaries, and visualizations that communicate findings and implications.
• Stay current with advancements in AI/ML, LLMs, and cybersecurity data domains.
• Contribute to MLOps practices including model versioning and deployment support.
• Assist in LLM-driven techniques for assessing compromised datasets to gauge impact and score risk.

Requirements

• 2–5+ years of professional experience as a Data Scientist with emphasis on AI/ML model development and deployment (0–3 years for Junior level).
• Bachelor's degree required; Master's preferred in Data Science, Computer Science, Statistics, Mathematics, or a closely related field.
• Proficiency in Python (primary); experience with R is a plus.
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and basic LLM implementation/fine-tuning (BERT, GPT-style models, LLaMA/Ollama, or equivalents).
• Strong proficiency in data manipulation and analysis (Pandas, NumPy) and SQL.
• Ability to translate technical outputs into clear, actionable insights and presentations.
• Familiarity with cybersecurity data types and concepts (CVEs, SBOMs, vulnerability severity scoring, risk exposure metrics) or enterprise risk management domains is beneficial.
• Active TS//SCI security clearance (or ability to obtain and maintain).
• Preferred certs: CISSP, CySA+, Security+, CEH
• Desired/Preferred Skills: Prior experience in cybersecurity, defense, national security, or enterprise risk management environments.
• Experience integrating AI/ML or LLM solutions with cloud platforms (AWS SageMaker, Azure ML, GCP) or on-premises systems.
• Knowledge of basic MLOps tools and practices.
• Experience with business intelligence and dashboard tools (Tableau, Power BI).
• Familiarity with big data frameworks or large-scale data processing.
• Additional programming skills (Java, C++) are a plus.
• Strong foundation in statistics, linear algebra, and experimental design.