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Data Scientist III

Location: Washington, DC

Company: Revolutional, LLC

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Title: Data Scientist III

Location: Washington, DC or Chandler, AZ

Terms: Full-time

Clearance: Secret eligibility 

Travel: 0-20%

Position Description

As a Data Scientist III at Revolutional, you sit at the intersection of advanced machine learning and federal cybersecurity operations. You design and build novel analytic methods, develop ML algorithms for cyber defense, and engineer data workflows that turn disparate, high-volume security data into actionable intelligence. You are a senior practitioner who leads technical design, drives implementation, and delivers solutions that measurably improve how security teams detect and respond to threats.

You bring deep quantitative expertise, strong engineering discipline, and the ability to operate in complex cloud and on-premise federal environments. You don’t just build models — you architect end-to-end data solutions, automate security tool workflows, and produce visualizations and dashboards that make your findings accessible to operational and executive audiences alike.

Responsibilities

• Architect, design, and lead implementation of novel analytic methods and machine learning solutions for cybersecurity defense purposes, including anomaly detection, threat classification, and behavioral analysis
• Develop and engineer data workflows that merge disparate structured and unstructured data sources, resolve data quality issues, and surface data-driven insights at scale
• Build and maintain ML algorithms, data models, and architecture strategies specifically designed to support cyber defense operations and threat detection capabilities
• Design and develop automation for security tools management; create customized searches, correlation rules, and applications within SIEM and SOAR platforms
• Apply advanced statistical techniques and mathematical analyses to solve complex cybersecurity problems across large, high-velocity data sets
• Leverage MITRE ATT&CK and D3FEND frameworks to inform ML model development, feature engineering, and analytic design for threat detection use cases
• Engineer containerized data solutions using Kubernetes and Docker; deploy and manage data pipelines in cloud environments across IaaS, SaaS, and PaaS models
• Develop data visualizations and dashboards to support high-visibility cybersecurity reporting for operational teams and executive leadership
• Lead technical design reviews and guide junior team members through implementation of complex data science solutions
• Collaborate with SOC, threat hunting, vulnerability management, and incident response teams to identify analytic gaps and translate operational needs into data solutions
• Ensure data integrity across all analytic workflows; apply rigorous validation and testing to ML models before operational deployment
• Stay current on advances in machine learning, AI security applications, and emerging data science methodologies relevant to the federal cybersecurity domain

What You Bring (Requirements) Baseline Requirements

• Master’s or PhD in Statistics, Engineering, Computer Science, Economics, or a related quantitative field; additional years of qualifying experience may be considered in lieu of the required degree on a year-for-year basis
• 5 or more years of experience in machine learning engineering, data science, data project engineering, or software development for data solutions
• Experience designing technical solutions and leading a team through implementation
• Secret eligibility required

Technical & Domain Capabilities

• Proficiency in object-oriented programming languages including one or more of: Python, Java, C++, R, or Scala; experience with JSON data structures
• Hands-on experience with containerization tools including Kubernetes and Docker for deploying and managing data pipelines
• Experience architecting and implementing novel ML methodologies for analytic and predictive use cases
• Demonstrated experience developing ML algorithms, data models, or architecture strategies for cybersecurity or cyber defense applications
• Experience engineering data workflows to integrate disparate data sources and produce reliable, actionable data products
• Experience designing and developing automation for security tool management, including customized searches and application development
• Working knowledge of SIEM and SOAR platforms and their data capabilities
• Experience with Cloud Service Provider solutions across IaaS, SaaS, and PaaS models in commercial or GovCloud environments
• Familiarity with host-based and network-based security technologies and the data they generate
• Knowledge of MITRE ATT&CK and D3FEND frameworks as applied to analytic design and cyber defense use cases
• Ability to perform rigorous analysis of structured and unstructured data using advanced statistical and mathematical techniques

Core Strengths

• Technically authoritative: you design solutions others implement and your architectural decisions hold up under operational and compliance scrutiny
• Analytically rigorous — you apply statistical discipline to everything you build and don’t ship models you can’t explain or validate
• Translates complex quantitative findings into clear visualizations and narratives for non-technical stakeholders
• Collaborative technical leader who raises the capability of the team and doesn’t operate as a lone contributor

Certifications

• Certified Analytics Professional (CAP) required

Nice to Have (Differentiators)

• Additional data science or ML certifications: AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or equivalent cloud ML credential
• Security certifications: CompTIA Security+, CySA+, or equivalent, demonstrating cross-domain fluency
• Experience applying ML/AI to cybersecurity problems in a federal civilian, defense, or intelligence environment
• Familiarity with AI/ML security risks including adversarial ML, model poisoning, and securing agentic or autonomous systems
• Experience with graph analytics, network analysis, or link analysis applied to threat detection
• Background in natural language processing (NLP) for log analysis or threat intelligence extraction
• Active Secret clearance

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