• This is a rare opportunity to define how data moves across the U.S. military’s logistics ecosystem. • As the Architecture and Data Lead, you will own one of the most critical integration challenges in modern DoD software: enabling data to flow reliably across platforms, classification levels, and operational environments — and transforming it into usable, decision-ready information. • You will design and build the integration platform that connects DEFCON AI’s software to the broader logistics C2 ecosystem. This includes developing adapters, common data models, translation layers, synchronization mechanisms, and classification-aware data architecture that ensure systems remain coherent from headquarters to the tactical edge. • This is a senior role combining deep technical execution and system-level architecture leadership. You will define the architecture, build key components, and guide a team of engineers and partners to deliver production-ready integrations across disconnected environments, varied data models, and multi-enclave deployments.

• This is a rare opportunity to define how data moves across the U.S. military’s logistics ecosystem. • As the Architecture and Data Lead, you will own one of the most critical integration challenges in modern DoD software. • You will design and build the integration platform that connects DEFCON AI’s software to the broader logistics C2 ecosystem. • This is a senior role combining deep technical execution and system-level architecture leadership.

• Partner with senior scientists to forecast cash flows and other drivers of business health using statistical and ML modeling. • Design and build models that make our capital products better for customers and deliver positive return assets. Sharper pricing, lower losses, growth and offers that fit how SMBs actually run. • Proactively mine our datasets for insight, and prototype new models that move the needle for customers and the business. • Own model outcomes end-to-end. Not just deployment bespoke to the platforms Pipe serves (e.g., UberEats and Housecall Pro), but ongoing performance, residual analysis, drift detection, and the judgment calls about when a model needs to be retrained, replaced, or retired. You'll work closely with product, risk peers and engineering to make this real in production.

• Independently developing and owning statistical models that forecast cash flows and other drivers of business and customer health. • Designing and building models that optimize our product offerings to better serve our customers' needs. • Proactively exploring datasets to surface insights, and prototyping new approaches that create measurable value for customers. • Taking full outcome ownership of deployed models, including performance monitoring, residual analysis, and iteration, in close collaboration with engineering. • Mentoring junior team members, raising the technical bar through code and design review, and contributing to our data science best practices and long-term tooling roadmap. • Partnering with engineering and data engineering to productionize models, shape data infrastructure requirements, and improve our MLOps foundations.