• Engage with customers as a senior voice in AI and security reviews • Lead discussions to understand and resolve customer needs • Develop thought leadership materials and frameworks • Collaborate closely with internal teams to bridge customer needs with product offerings

• Own the performance and outcome of all deployed statistical and machine learning models, ensuring a seamless translation from data science output to effective underwriting decisions and measurable portfolio results. • Establish rigorous monitoring and feedback loops between model outputs and real-world credit outcomes. • Drive use of alternative data (transaction-level signals, partner platform data, behavioral indicators) to improve model accuracy and expand approval rates. • Own model risk governance including model documentation, validation processes, and ongoing performance standards across all deployed models. • Direct the end-to-end credit strategy, translating risk models and data insights into a cohesive set of underwriting policies, approval strategies, and pricing frameworks to deliver target loss and growth performance. • Own approval logic, pre-qualification strategies, and credit limit frameworks that balance growth targets, merchant experience, and credit loss thresholds. • Set minimum acceptance criteria aligned with Pipe's risk appetite while optimizing approval rates, customer lifetime value, and capital facility performance. • Design and implement pricing frameworks to ensure appropriate loss coverage and positive borrower selection. • Own portfolio performance reporting and forward-looking risk adjustments across all credit products. • Build early-warning systems and structured decision processes that enable fast, disciplined credit strategy adjustments. • Ensure portfolio resilience across economic cycles through proactive monitoring and timely action. • Act as the primary owner of our data quality roadmap, identifying discrepancies and driving technical solutions to resolve them. • Proactively identify and acquire net-new data sources (internal or third-party) to solve for missing signals and improve model accuracy. • Translate business requirements into clear data engineering and infrastructure specs, ensuring we have the right inputs for reliable, scalable risk decisioning. • Work closely with Risk Operations to ensure credit strategy is implemented reliably in production systems and iterated quickly based on portfolio results. • Partner with Capital Markets and Finance on loss forecasting, reserve setting, and risk appetite frameworks; manage credit policy to maintain compliance with facility covenants and triggers. • Collaborate with Product and Engineering to build scalable decisioning infrastructure that enables rapid strategy iteration. • Lead a team of seven risk specialists responsible for model building, credit strategy translation, and portfolio monitoring. • Create clarity, momentum, and accountability while maintaining a sustainable pace of execution. • Build a culture of analytical rigor, pragmatism, and bias for action.

• Mitigar el riesgo operativo • Establecer el control metodológico • Desplegar rituales de alto rendimiento basados en datos • Diseñar, estructurar e implementar marcos sólidos de gobierno de procesos a nivel corporativo • Definir e iterar matrices de control y marcos normativos

• Own and optimize margin calculations across all supported modes, including Isolated, Cross, and Portfolio Margin. • Develop and refine risk parameters (maintenance margin, initial margin, and haircut models) to ensure the exchange is robust against extreme market volatility. • Conduct stress testing and back-testing on margin models to ensure they withstand "black swan" events. • Perform post-mortem analysis on trader liquidations to identify patterns of failure. • Investigate cases of negative equity (socialized losses/ADL) to determine why margin was insufficient and recommend adjustments to the insurance fund or liquidation engine logic. • Partner with the engineering team to design and build real-time Risk Dashboards that monitor exchange-wide health, leverage ratios, and margin utilization. • Translate complex risk requirements into technical specifications for the development of automated risk-mitigation tools. • Collaborate with the Customer Experience (CX) team to investigate trader complaints regarding high slippage or liquidity constraints. • Analyze order book depth and execution quality to understand how liquidity gaps contribute to liquidation cascades.