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.
What You'll Do


* 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...