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.