DATA SCIENTIST (CYBER/CLOUD)
About The Position
Quantum Research International, Inc. (Quantum) is seeking a motivated Mid-Level (or Junior) Data Scientist to support advanced AI/ML and large language model (LLM) initiatives within a cybersecurity and risk analysis platform. The role involves contributing to the development, deployment, and integration of predictive models and LLM-based solutions to process structured and unstructured data, generate actionable insights, and support data-informed decision making in a high-stakes defense environment. Quantum provides services and products in areas such as Cybersecurity and Information Operations, Space Operations and Control, Aviation Systems, Defense Systems, Intelligence Programs Support, Experimentation and Test, Program Management, and Audio/Visual Technology Applications. The company has a corporate office in Huntsville, AL, and actively hires nationwide and internationally, with physical locations in Aberdeen, MD; Colorado Springs, CO; Crestview, FL, Orlando, FL, and Tupelo, MS.
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
Nice To Haves
- 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.
- This role combines technical AI/ML and LLM contributions with collaboration in a sensitive, cleared environment. Ideal candidates are proactive, eager to learn, and comfortable applying advanced modeling to real-world cybersecurity challenges.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
