Cybersecurity Data Scientist

Booz Allen Hamilton•McLean, VA

3d•$77,600 - $176,000•Onsite

About The Position

As a Cybersecurity Data Scientist, you will operate as a hands-on technical contributor and applied research leader responsible for designing, developing, and operationalizing data-driven and AI-enabled solutions for Booz Allen's Cyber Operations teams. This role emphasizes execution and delivery, turning security telemetry, threat intelligence, and analyst workflows into production-grade models, detections, and decision-support capabilities that measurably improve prevention, detection, response, and recovery outcomes. You will bridge data science and security operations by translating analyst needs, threat models, and incident learnings into reproducible data pipelines, feature sets, ML/LLM models, and evaluation frameworks deployed across cloud, network, endpoint, identity, and application telemetry domains. You will originate, facilitate, and lead cross-functional efforts to mature AI-enabled cybersecurity capabilities, including detection engineering augmentation, alert triage, threat hunting, and SOC automation, while guiding teams through threat-informed model development, secure-AI engineering, and responsible AI practices. Perform model and solution reviews, provide technical direction for complex analytics initiatives, including SIEM, SOAR, and EDR data science integrations, cloud-native security analytics, and GenAI tooling for analysts, and translate findings into actionable, measurable implementation plans. Leverage strong analytical, statistical, and communication skills to assess complex security and business problems, align technical and non-technical stakeholders, and drive decisions to closure in support of Booz Allen Hamilton's critical enterprise infrastructure, go-to-market platforms, and mission operations. The ideal candidate for our Enterprise Cybersecurity team is technically inclined, intellectually curious, and adaptable, with a strong cyber-defense mindset. They thrive in a fast-paced, dynamic environment and are continuous learners who actively seek to understand complex challenges, ask thoughtful questions, and look beyond the obvious to identify innovative and effective ways of working. They bring a security-first perspective, analytical problem-solving skills, and the curiosity and aptitude to continuously evolve as threats, technologies, and mission needs change. This position is located in McLean, VA.

Requirements

5+ years of experience in data science, machine learning engineering, or applied AI
3+ years of experience leading cross-functional ML or analytics initiatives, including cybersecurity or security operations
Experience designing and implementing data science and AI/ML solutions over enterprise security telemetry spanning network, endpoint, application, identity, and cloud environments
Experience developing, testing, and integrating ML and analytic capabilities across security tools and platforms using APIs, automation, and workflow orchestration
Experience with software development in Python and SQL for security and AI/ML use cases, including production-quality code, unit and integration testing, version control, and CI/CD
Experience with the modern AI/ML stack, including at least 2 of the following: PyTorch or TensorFlow, scikit-learn, Hugging Face, LangChain, LlamaIndex, vector databases, such as pgvector, OpenSearch, Pinecone, or Milvus, or embedding-based retrieval
Experience operationalizing AI/ML systems, such as MLOps, including model versioning, experiment tracking, evaluation harnesses, drift and quality monitoring, and CI/CD for models, such as MLflow, Weights and Biases, SageMaker, Vertex AI, Azure ML, or Kubeflow
Experience applying AI and machine learning to cybersecurity use cases such as threat and anomaly detection, behavioral analytics, alert triage and prioritization, threat hunting support, analyst copilots, and response automation with an impact on SOC outcomes
Ability to obtain a Secret clearance
Bachelor's degree

Nice To Haves

Experience with programming or scripting languages used in security and automation environments, such as Python, Go, SQL, PowerShell, or Bash
Experience designing, deploying, and maintaining enterprise-scale security solutions for sensitive or regulated environments, such as FedRAMP, IL4/5, HIPAA, or PCI
Experience designing and building agentic AI systems for security operations, including multi-step reasoning, tool and function calling, retrieval pipelines, and human-in-the-loop workflows
Experience fine-tuning, distilling, or evaluating LLMs and other models for domain-specific security tasks, including building eval datasets and red-teaming AI systems
Experience evaluating and integrating AI-enabled cybersecurity tooling, such as AI-assisted SIEM/SOAR, UEBA, behavioral analytics, and model-driven detection workflows into enterprise security operations
Knowledge of AI governance, model risk management, and policy controls aligned to enterprise and regulatory expectations for responsible AI use
Knowledge of data governance frameworks, data classification standards, and privacy regulations, such as GDPR, or CCPA
Knowledge of database structures, data modeling fundamentals, and query optimization, including SQL and NoSQL platforms
IT Engineering or Security Certifications, such as CISSP, CCSP, CDPSE, cloud security certifications, or relevant AI security certifications such as ISC2 CAISS or IAPP AIGP

Responsibilities

Design, build, and deploy custom AI/ML solutions for cybersecurity, including supervised and unsupervised detection models, anomaly and behavioral analytics, NLP on security text, retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM-assisted analyst tooling, and operationalize them end-to-end: data ingest, feature engineering, training/tuning, evaluation, deployment, monitoring, and retraining.
Engineer scalable data pipelines over security telemetry, including logs, EDR, network, identity, cloud, and threat intel, to produce high-quality, labeled, and feature-rich datasets that power detection, triage, and hunting use cases.
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate model performance, reduce analyst burden, and quantify operational impact.
Apply secure-AI and MLSecOps engineering practices throughout the AI/ML lifecycle, including model and data protection, prompt and inference risk mitigation, evaluation against adversarial inputs, including evasion, poisoning, and prompt injection, and responsible AI controls.
Integrate models and analytics into security tools and workflows, such as SIEM, SOAR, EDR, IAM, CSPM) — extending detection logic, enrichment, and response playbooks with custom ML/LLM capabilities where commercial tooling falls short.
Develop automation, scripting, and infrastructure-as-code (IaC) to enable repeatable, testable, and version-controlled ML pipelines, model deployments, and security data integrations.
Collaborate across engineering, platform, data, threat intelligence, and SOC operations teams to deliver end-to-end solutions, embed security and ML practices into DevSecOps and MLSecOps pipelines, and drive implementation through measurable operational outcomes.