• Establish the Gold Standard: Design and implement comprehensive scorecards and benchmarking suites for LLM-based extraction, summarization, and chat interfaces. • Bridge the Gap with SMEs: Act as the technical lead in working with Subject Matter Experts (SMEs) to codify their expertise into evaluation datasets and "ground truth" labels. • Scale Labeling: Design the statistical guardrails to scale both our human and automated labeling efforts. • Quantify Risk: Provide clear, data-driven "Go/No-Go" recommendations for model deployment based on rigorous error analysis and statistical confidence intervals.

• Lead the strategy and application of machine learning and advanced analytics across payments and treasury • Own end-to-end delivery and cross-functional execution • Set technical standards and scalable frameworks for analytical and modeling capabilities • Communicate insights and recommendations clearly to stakeholders

• Build industry-leading machine learning models for managing credit and fraud risks • Leverage multiple complex data sources such as credit bureau reports and customer supplied information at large scale • Collaborate with engineers to deploy your models into a production environment • Propose and execute solutions to various problems within business constraints • Use responsible AI technique following regulatory requirements and lending best practices • Actively monitor the credit risk models in production • Extract the most value out of data to significantly impact our key business metrics • Conduct ad-hoc analysis related to risk management, investor services, operations and corporate development

• Work independently with cross functional teams to develop software and system requirements. • Design, implement, and deploy state-of-the-art machine learning models. • Monitor the performance of the ML models and drive continuous improvement. • Lead team code quality activities including design and code reviews. • Communicate complex analytical findings and model performance metrics to both technical and non-technical stakeholders through clear visualizations and presentations. • Provide technical guidance to team members.