DATA SCIENTIST-SENIOR

Quantum Research International Inc•Huntsville, AL

1d•Onsite

About The Position

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; and Tupelo, MS Mission: Quantum Research is seeking an experienced Senior Data Scientist to lead advanced AI/ML and large language model (LLM) initiatives within a cybersecurity and risk analysis platform. You will develop, deploy, and integrate predictive models and LLM-based solutions to process structured and unstructured data (including SBOMs, CVEs, vulnerability data, and critical program information), generate actionable insights, produce executive-level natural language summaries, and drive data-informed decision making in a high-stakes defense environment.

Requirements

5–8+ years of professional experience as a Data Scientist with strong emphasis on AI/ML model development, deployment, and productionization.
Advanced degree (Master’s or PhD preferred) in Data Science, Computer Science, Statistics, Mathematics, or a closely related field.
Expert-level proficiency in Python (primary); experience with R is a plus.
Hands-on experience with major ML frameworks (TensorFlow, PyTorch, Scikit-learn) and LLM implementation/fine-tuning (BERT, GPT-style models, LLaMA/Ollama, or equivalents).
Strong proficiency in data manipulation and analysis (Pandas, NumPy) and SQL (NoSQL experience beneficial).
Demonstrated ability to translate complex technical outputs into clear, actionable, business-oriented insights and presentations for non-technical audiences.
Proven track record of leading data-science projects end-to-end and mentoring junior team members.
Familiarity with cybersecurity data types and concepts (CVEs, SBOMs, vulnerability severity scoring, risk exposure metrics) or enterprise risk management domains.
Active TS//SCI security clearance (or ability to obtain and maintain TS/ SCI with Secret)

Nice To Haves

Prior experience in cybersecurity, defense, national security, or enterprise risk management environments.
Hands-on work integrating AI/ML or LLM solutions with cloud platforms (AWS SageMaker, Azure ML, GCP) or on-premises systems.
Knowledge of MLOps tools and practices (model registries, CI/CD for ML, monitoring, versioning).
Experience with business intelligence and dashboard tools (Tableau, Power BI).
Familiarity with big data frameworks (Hadoop ecosystem) or large-scale data processing.
Experience with AI model robustness testing and secure data pipeline design.
Additional programming skills (Java, C++) are a plus.
Experience with adversarial Tactics, Techniques, and Procedures (TTPs) as well as data poisoning and degradation detection and secure model deployment.
Strong foundation in statistics, linear algebra, and experimental design.

Responsibilities

Lead the formulation, design, and management of data-driven projects focused on cybersecurity risk prioritization, vulnerability analysis, exposure trends, anomaly detection, and automated risk reporting.
Develop, fine-tune, and deploy 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).
Integrate model outputs into data pipelines, dashboards, and visualization tools to deliver clear, intuitive, high-level insights for technical and non-technical stakeholders (executives, program managers).
Collaborate closely with data analysts, software developers, and cross-functional teams to embed predictive analytics, LLM capabilities, and automated prioritization into existing platforms.
Clean, collate, and preprocess complex datasets from multiple sources; assess and improve data quality and new data gathering techniques.
Select and apply advanced statistical methods, machine learning algorithms, and LLM techniques to extract actionable insights.
Perform model validation, cross-validation, performance monitoring, and robustness testing against evolving cybersecurity threats.
Optimize models for accuracy, scalability, interpretability, and production readiness (balancing performance with usability for non-experts).
Mentor and provide technical guidance to junior data scientists; delegate tasks, monitor performance, and foster a collaborative team environment.
Produce and present non-technical reports, executive summaries, and visualizations that clearly communicate model successes, limitations, and business implications.
Stay current with advancements in AI/ML, LLMs, MLOps, and cybersecurity data domains to ensure solutions remain cutting-edge and relevant.
Contribute to MLOps practices including model versioning, deployment pipelines, and cloud/on-prem integration.
Develop and implement LLM-driven techniques for assessing compromised datasets to gauge impact and score risk.