Data Science Lead

ThreatConnect

full-time

Posted on: 9/17/2025

Origin: • 🇺🇸 United States • Virginia

✨ AI Apply

Senior

CloudCyber SecurityNumpyPythonScikit-Learn

About the role

Model Development: Lead design and implementation of probabilistic and statistical models for loss magnitude, frequency, and attack path likelihood
Data Leadership: Expand and curate ThreatConnect’s risk data sets, including loss event data, CVE data (KEV, EPSS), MITRE ATT&CK coverage, control posture data, and third-party risk data
Player-Coach: Mentor a team of data scientists while remaining hands-on with modeling, code reviews, and experimentation
Cross-Functional Collaboration: Partner with Product, Engineering, and Threat Intelligence teams to operationalize models in RQ
Innovation: Research and apply advanced methods (Bayesian modeling, ML techniques) to continuously improve prediction accuracy and coverage
Quality & Governance: Ensure model transparency, explainability, and defensibility for customer and regulatory review. Lead the development of algorithmic models for CRQ, including threat likelihood, loss magnitude, control efficacy, and scenario simulation
AI: Guide the implementation of AI-enhanced modeling (e.g., LLMs, pattern mining) to support automation of risk scenario development and decision support

7+ years of experience in applied data science, quantitative modeling, or algorithm development
Strong understanding of cybersecurity principles, threat actor behavior, or risk frameworks (e.g., NIST CSF, MITRE ATT&CK, FAIR)
Proven ability to build and deploy risk or predictive models in enterprise environments
Proficiency in Python and familiarity with modeling libraries (e.g., NumPy, PyMC3, scikit-learn)
Experience with Git, Jira, and modern ML ops pipelines
Strong communication and storytelling skills for technical and non-technical audiences
Experience building CRQ models in alignment with FAIR or related frameworks (desired)
Familiarity with simulating attack paths, graph-based reasoning, or control validation (desired)
PhD or advanced degree in data science, computer science, engineering, or related field (desired)
Experience with integrating models into SaaS platforms or cloud-native environments (desired)
Background in red/blue teaming, SOC data, or adversary emulation is a plus (desired)