Data Science & Advanced Analytics

Introduction

Since 1973, East West Bank has served as a pathway to success. With over 110 locations across the U.S. and Asia, we are the premier financial bridge between the East and West. Our teams of experienced, multi-cultural professionals help guide businesses and community members on both sides of the Pacific looking to explore new markets and create new opportunities, and our sustained growth and expertise in industries like real estate, entertainment and media, private equity and venture capital, and high-tech help build sustainable businesses and expand our associates’ potential for career advancement.

Headquartered in California, East West Bank (Nasdaq: EWBC) is a top-performing commercial bank with a strong foundation, an enterprising spirit and a commitment to absolute integrity. East West Bank gives people the confidence to reach further.

East West Bank is seeking a highly experienced Data Science & Advanced Analytics to lead enterprise-scale AI, machine learning, and advanced analytics initiatives that drive measurable business outcomes across the bank.

This role is designed for a hands-on, execution-oriented leader with deep expertise in data-driven decisioning, scalable business analytics, and AI-enabled process transformation within highly regulated industries. The ideal candidate combines strong technical depth with practical business acumen and has a proven track record building production-grade analytics solutions that improve operational efficiency, revenue growth, customer experience and risk management.

The role partners closely with business, technology, data engineering, risk, compliance, and operations teams to operationalize AI and analytics capabilities across critical banking functions.

• Lead the design, development, and deployment of enterprise AI, machine learning, and advanced analytics solutions across key banking domains including risk, fraud, AML/BSA, customer analytics, cross selling and operational intelligence.
• Drive end-to-end analytics delivery from business problem definition through data engineering, feature engineering, model development, deployment, monitoring, and business adoption.
• Build scalable and production-grade data science and machine learning capabilities leveraging Azure-native and distributed computing frameworks including Azure ML, Databricks, Spark, and cloud-based data platforms.
• Operationalize developed solutions within core business processes and decision workflows to drive measurable business value and adoption.
• Partner with engineering teams to integrate models into enterprise systems through APIs, microservices, and modern data platforms.
• Drive model governance, explainability, monitoring, validation, recalibration, and regulatory compliance activities aligned with banking and model risk expectations.
• Establish best practices for tech stack choices, MLOps, model lifecycle management, CI/CD automation, experiment tracking, and production monitoring.
• Collaborate cross-functionally with business, risk, compliance, legal, audit, and technology stakeholders to ensure responsible and scalable AI adoption.
• Mentor and lead high-performing analytics and data science teams, including distributed or offshore resources where applicable.
• Translate complex analytical insights into executive-level recommendations and measurable business outcomes.
• Perform other duties as assigned.

• 10+ years of hands‑on experience in data science, advanced analytics, AI/ML engineering, or quantitative modeling, including leadership experience within financial services, fintech, insurance, or other regulated industries.
• Proven track record delivering production-grade AI and analytics solutions with measurable business impact in complex enterprise environments.
• Deep hands‑on expertise in Python, SQL, machine learning frameworks, statistical modeling, predictive analytics, and distributed data processing.
• Strong practical experience with modern AI/ML tooling and platforms including Azure ML, Databricks, Spark, Tensor Flow, PyTorch, scikit‑learn, XGBoost, MLflow, and cloud‑native analytics ecosystems.
• Experience implementing scalable MLOps…