ThreatConnect

Customer Success Manager

• Drive the adoption and integration of the Risk Quantification (RQ) platform across the enterprise customer base
• Lead a team focused on delivering measurable value and embedding cyber risk quantification into ERM, board reporting, and decision-making workflows
• Serve as a trusted advisor and partner with Customer Success and Sales to drive product adoption, retention, and expansion
• Develop best practices to scale delivery and establish consulting methodologies
• Oversee customer support initiatives, ensuring customer issues and queries are addressed promptly and effectively
• Contribute to and expand a comprehensive knowledge base to empower self-service and proactive issue resolution
• Build a scalable consulting and support practice to ensure high retention and expansion
• Work closely with Product and Engineering to relay customer feedback and inform roadmap priorities
• Support pre-sales efforts through scoping calls, solution design, and value articulation
• Establish consulting and customer support KPIs and continuously improve delivery metrics and customer satisfaction

Director of Customer Success

Data Scientist

• 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

Director of Customer Success

Data Science Lead