Applied AI/ML Data Scientist - Vice President

As a VP AI/ML Data Scientist in CIB's Global Banking & Payments group, you will translate complex banking challenges into scalable, production-grade AI/ML and LLM solutions. Partnering with stakeholders across Global Banking & Payments, front office, Product, and Client Onboarding & Service (COS), you'll build prototypes and deliver governed models and intelligent agents that improve origination velocity, revenue quality, client engagement, operational efficiency, and risk reduction. Job Summary • Define & deliver high-value use cases with Global Banking & Payments stakeholders — prospecting and wallet-share models, fee/revenue forecasting, deal probability, investor/counterparty mapping, onboarding triage, service case routing, and execution analytics. • Build COS Agents to automate Client Onboarding & Service workflows — document intake/QC, KYC data extraction, case summarization, and multi-step resolution. • Develop LLM solutions using retrieval-augmented generation, agent orchestration, prompt engineering, guardrails, and red-teaming to deliver reliable, explainable outcomes. • Own end-to-end pipelines: data profiling, feature engineering, model development, evaluation, fairness/explainability, and production deployment in cloud and hybrid environments. • Implement MLOps: version control, model registry, CI/CD, containerization, automated testing, monitoring, drift detection, and incident/rollback procedures. • Leverage cloud data platforms: AWS (EKS, EC2, Lambda), query engines (Starburst/Trino), data warehouses (Redshift), and graph databases (Neptune). • Ensure governance & compliance — enforce data access controls, privacy requirements, secure compute, and lineage throughout the model lifecycle. • Drive adoption: run A/B tests, capture user feedback, mentor junior team members, and champion responsible AI practices. Required Qualifications • 7–10+ years building and deploying ML models in production, ideally in banking, payments, or similarly regulated domains. • Strong Python & SQL; proficiency with pandas, NumPy, scikit-learn, XGBoost, and at least one deep learning framework (PyTorch or TensorFlow); solid software engineering practices. • MLOps experience: containerization/orchestration, experiment tracking, model registries, monitoring, drift detection, and structured change management. • Cloud fluency: AWS services (EKS, EC2, Lambda), distributed query engines, and data warehousing. • Stakeholder management: proven ability to translate banking workflows and commercial objectives into technical requirements; strong communication across front office, Product, risk, compliance, and technology. • Data governance awareness: familiarity with KYC/AML context and model risk frameworks. Preferred Qualifications • Experience supporting Global Banking & Payments and COS stakeholders. • Hands-on with LLMs and agentic systems: RAG, structured outputs, tool use, guardrails/safety, and evaluation frameworks. • Experience with graph analytics, NLP, and time-series modeling for prospecting, network analysis, and forecasting. • Familiarity with feature stores, A/B testing, and performance/cost optimization at scale. • Advanced degree in a quantitative field (Computer Science, Statistics, Mathematics, Engineering, or quantitative Finance/Economics) or equivalent experience.