Data Scientist Team Lead

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist Team Lead based in United States.

This role offers the opportunity to lead advanced data science initiatives at the intersection of machine learning, cybersecurity, and enterprise analytics. You will design data models, analytical workflows, and automation capabilities that strengthen security monitoring, threat detection, and proactive defense. Working alongside security operations, threat hunting, cyber intelligence, penetration testing, digital forensics, and engineering teams, you will transform complex security telemetry into actionable insights. The position combines technical leadership with hands-on development across large and disparate enterprise datasets. You will help develop predictive models, behavioral analytics, risk analysis, and scalable data pipelines supporting mission-critical cybersecurity operations. This is an impactful opportunity to apply advanced data science techniques to complex cyber defense challenges at enterprise scale.

Accountabilities:

• Lead the design and implementation of technical data science solutions supporting enterprise cybersecurity operations.
• Develop data models, machine learning capabilities, and automation that enhance security monitoring, threat detection, threat hunting, User and Entity Behavior Analytics (UEBA), and cybersecurity information correlation.
• Design custom algorithms, analytical processes, and data architectures for complex enterprise-scale datasets used in modeling, data mining, research, and cyber defense.
• Develop advanced analytics for predictive risk analysis, risk identification and mapping, cyber risk cost analysis, supply chain threat identification, and behavioral analytics.
• Design and implement data pipelines and workflows that ingest, normalize, correlate, and analyze information from diverse security and enterprise data sources.
• Develop machine learning models that identify anomalous behavior, emerging cyber threats, attack patterns, and other indicators of malicious activity.
• Create security metrics and trend analyses covering anomalies, threat actor activity and impact, user behavior, and other cybersecurity indicators.
• Apply data science and automation to cyber threat hunting to increase analytical maturity and reduce manual investigative effort.
• Incorporate data science models into operational threat hunting and predictive threat simulations designed to estimate attack probability and potential cost or impact.
• Correlate cyber threat intelligence with assets, users, and security events to assess the potential financial and operational impact of cyber incidents.
• Develop custom Indicators of Compromise (IOC) and Indicators of Attack (IOA) detection and alerting capabilities.
• Create visualizations, dashboards, and high-visibility reporting that communicate complex cybersecurity data, trends, risks, and analytical findings to both technical and leadership audiences.
• Design and develop automation supporting security tool administration, cybersecurity analytics, customized searches, and security applications.
• Support integration of analytical capabilities with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms.
• Support the development, integration, optimization, and maintenance of security data analytics and UEBA capabilities.
• Identify and resolve critical data quality, integrity, architecture, and analytical issues affecting cybersecurity capabilities.
• Establish repeatable and scalable analytical processes that improve detection, investigation, response, and proactive cyber defense operations.
• Provide technical leadership by translating complex requirements into practical solutions and guiding teams through implementation.

Requirements:

• Master’s or PhD in a quantitative field such as Statistics, Engineering, Computer Science, or Economics; additional relevant experience may be considered in lieu of the required education on a year-for-year basis.
• 5+ years of experience in machine learning engineering, data science, data project engineering, or software development for data-focused solutions.
• Demonstrated experience designing technical solutions and leading teams through their implementation.
• Proficiency with object-oriented programming and data-oriented technologies such as Python, JSON, C++, Java, R, or Scala.
• Experience with containerization technologies, including Kubernetes and Docker.
• Experience architecting and designing novel analytical methods using machine learning methodologies.
• Experience developing and supporting data solutions for cybersecurity, including machine learning algorithms, data models, or architecture strategies for cyber defense.
• Experience engineering data workflows that merge disparate data sources and generate actionable, data-driven insights.
• Experience designing and developing automation for security tool management, customized searches, and cybersecurity applications.
• Ability to develop high-visibility cybersecurity reporting through data visualizations, dashboards, and analytical presentations.
• Strong analytical, problem-solving, and technical communication skills, with the ability to collaborate across cybersecurity and technical disciplines.
• Certified Analytics Professional (CAP) certification required.
• Must be eligible to obtain a Secret security clearance.

Benefits:

• Competitive salary paid twice per month.
• Best-in-class medical coverage.
• 100% of medical insurance premiums covered by the employer.
• Company-wide new business incentive programs.
• Contribution incentive programs for activities such as white papers, blog posts, and internal webinars.
• 3 weeks of PTO starting annually.
• 11 paid holidays per year.
• 401(k) program with a 100% company match on the first 4% contributed.
• Monthly reimbursement for cell phone and home internet costs.
• Paternity and maternity leave.
• Investment in training and professional certifications to broaden and deepen technical expertise.
• Opportunity to work on enterprise-scale cybersecurity, machine learning, and data analytics initiatives.
• Collaboration with security operations, threat intelligence, threat hunting, digital forensics, penetration testing, and cybersecurity engineering teams.
• Opportunity to develop advanced analytics and automation capabilities supporting proactive cyber defense.