• Own and execute the multi-year technical roadmap for risk modeling, translating advanced simulation and statistical methods into production-grade analytics. • Define rigorous modeling methodologies, including assumptions, calibration, sensitivity analysis, and documentation suitable for external stakeholder and rating agency review. • Serve as the primary technical lead for executive briefings and deep-dive workshops, helping stakeholders operationalize model outputs. • Partner directly with Product and Engineering teams to integrate research prototypes and custom modeling solutions into cloud-hosted simulation platforms. • Drive go-to-market initiatives by articulating complex model differentiation, appropriate use cases, and limitations during RFPs and technical demos.

• Own the end-to-end execution of AI tools, from development through production, ensuring systems are reliable, scalable, and used in real workflows • Build and deploy AI-powered tools and backend logic for member-facing products (e.g., VSL Generator, GTM Planner, Sales Script Builder) • Integrate systems across CRMs, automation platforms, and third-party APIs while maintaining clean, reliable data flow • Develop and maintain automation across core operations (onboarding, billing, renewals, access, fulfillment, and revenue verification) • Build and manage data pipelines and systems that power real-time dashboards for growth, engagement, retention, and revenue • Support AI-driven sales, marketing, content, and community systems, including campaign automation, lead scoring, and content workflows • Write clean, maintainable code, follow architecture standards, proactively identify tradeoffs, and fully document systems, APIs, and workflows

• Own our most complex and valuable agent surface area. • Iterate, innovate, and drive adoption, rapidly evolving our product to stay ahead of the market. • Collaborate with the CPO and cross-functional partners.

• Design, implement, and maintain scalable infrastructure for ML and GenAI applications • Deploy, operate, and troubleshoot production ML/GenAI pipelines/services • Build and optimize CI/CD pipelines for ML model deployment and serving • Scale compute resources across CPU/GPU architectures to meet performance requirements • Implement container orchestration with Kubernetes • Architect and optimize cloud resources on GCP for ML training and inference • Setup and maintain runtime frameworks and job management systems (Airflow, KubeFlow, MLflow, etc.) • Establish monitoring, logging and alerting for systems observability • Optimize system performance and resource utilization for cost efficiency • Develop and enforce AIOps best practices across the organization