DATA SCIENTIST (CYBER/CLOUD)
- Compensation
- Not disclosed
- Location
- HUNTSVILLE, AL, HUNTSVILLE, AL, US
- Work setup
- Not specified
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 0+ years
- Category
- Data science
- Posted
Skills
Must have
- 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 have
- 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 model
Full description
Job Description
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English (US)
Job Posting TitleDATA SCIENTIST (CYBER/CLOUD)
Job Description
Overview:
Quantum Research International, Inc. (Quantum) provides our national defense and federal civilian and industry customers with services and products in the following main areas: 1) Cybersecurity and Information Operations; 2) Space Operations and Control; 3) Aviation Systems; 4) Ground, Air and Missile Defense, and Fires Support Systems; 5) Intelligence Programs Support; 6) Experimentation and Test; 7) Program Management; and (8) Audio/Visual Technology Applications. Quantum's Corporate Office is located in Huntsville, AL, but Quantum actively hires for positions nationwide and internationally. We pride ourselves on providing high quality support to the U.S. Government and our Nation's Warfighters. In addition to our corporate office, we have physical locations in Aberdeen, MD; Colorado Springs, CO; Crestview, FL, Orlando, FL, and Tupelo, MS
Mission:Quantum Research Int 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. You will contribute to the development, deployment, and integration of predictive models and LLM-based solutions to process structured and unstructured data (including SBOMs, CVEs, vulnerability data, and critical program information), generate actionable insights, and support data-informed decision making in a high-stakes defense environment.
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.