• Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives. • Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights. • Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy. • Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization. • Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise. • Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward. • Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.

• Help teach our AIs to think • Dive into data and discover patterns • Train models and create decision logic • Implement LangChain and build autonomous agents • Support the end-to-end lifecycle of a Data Science project

• 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