• Lead the architecture, implementation, and long-term roadmap for core systems which support multiple fraud prevention use cases.
• Own the end-to-end delivery of large cross-function projects, translating ambiguous high impact problems into strategy and execution, make pragmatic tradeoffs, and drive teams to measurable outcomes.
• Design, build, and operate highly available, low-latency, backend systems that enable real-time scoring and decisioning for fraud prevention.
• Partner with Data Science and ML teams to productionize models, build reliable ML data pipelines, and enable real-time model serving.
• Establish and elevate department level best practices, review designs, drive engineering quality, and act as a trusted advisor on architectural tradeoffs.
• Mentor and grow engineers, documenting learnings and sharing technical direction through writing, 1:1s, and team meetings
• Navigate and influence multiple stakeholders, help align teams, communicate tradeoffs to technical and non-technical partners, and independently resolve cross team issues.