Data Science at East West San Gabriel, California

Data Science Manager

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.

We are currently seeking a Data Science Manager to lead the development and deployment of advanced analytics solutions that drive measurable business outcomes across the bank. This role is responsible for establishing scalable data science workflows, operationalizing machine learning models, and embedding data-driven decisioning into core banking functions. The position sits at the intersection of business strategy, data engineering, and model development, ensuring that advanced analytics initiatives are aligned with enterprise priorities, regulatory requirements, and production-grade standards.

Day-to-Day Responsibilities

• Lead the design, development, and deployment of advanced analytics and machine learning solutions across key banking domains (e.g., credit risk, fraud, marketing, customer analytics), driving measurable business outcomes.
• Establish and scale end-to-end data science workflows across the model lifecycle, including data ingestion, feature engineering, model development, validation, deployment, and monitoring (MLOps).
• Develop and deploy machine learning models using Python (pandas, scikit-learn, XGBoost, PyTorch/TensorFlow) and SQL, ensuring production-grade performance, scalability, and maintainability.
• Build and maintain data science pipelines leveraging Azure-native and distributed computing frameworks (e.g., Azure ML, Databricks, Spark), supporting both batch and real-time inference.
• Collaborate with data engineering and application development teams to integrate models into production systems via APIs, microservices (FastAPI/Flask), or enterprise decision platforms, ensuring low-latency and scalable deployment.
• Drive model lifecycle management and governance, including model validation, performance tracking, monitoring, and periodic recalibration in alignment with regulatory expectations.
• Partner with data engineering teams to design and optimize feature stores and curated datasets, ensuring consistency between training and production environments.
• Implement best practices for model explainability, fairness, and auditability, supporting internal governance and regulatory compliance requirements.
• Mentor and develop junior data scientists through technical guidance, code reviews, and hands-on coaching; manage distributed team resources, including offshore capabilities, as needed.
• Promote adoption of analytics solutions by embedding models into business processes and decision workflows and partnering with stakeholders to drive measurable impact.

Required Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• 8-12+ years of experience in data science, machine learning, or advanced analytics, with at least 3+ years in a leadership or managerial capacity within financial services or other regulated industries.
• Strong hands-on proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, PyTorch/TensorFlow) and SQL, with demonstrated experience building and deploying production-grade machine learning models.
• Deep experience working within an Azure-based data and analytics ecosystem, including:
• Azure Machine Learning (Azure ML)
• Azure Databricks / Spark
• Azure Data Factory or Synapse Pipelines
• Azure Data Lake Storage (ADLS Gen2)
• Proven experience implementing MLOps frameworks, including model versioning, experiment tracking, CI/CD pipelines (Azure DevOps or GitHub Actions), and automated deployment.
• Strong understanding of the end-to-end machine learning lifecycle, including feature engineering, model validation, deployment, and post-production monitoring.
• Experience working with large-scale distributed data processing frameworks (e.g., Spark) and complex datasets.
• Solid foundation in statistical modeling and machine learning techniques, including regression, classification, clustering, time series analysis, and ensemble methods.
• Experience designing and implementing model monitoring frameworks, including performance metrics (AUC, KS, precision/recall) and data/model drift detection.
• Experience integrating models into production systems via APIs or microservices, and collaborating with engineering teams on scalable system design.
• Strong understanding of model risk management and regulatory expectations (e.g., SR 11-7), including documentation, validation, and audit readiness.
• Strong communication skills with the ability to translate complex analytical insights into business recommendations for senior stakeholders.

Preferred Qualifications

• Master's or PhD in a quantitative discipline.
• Deep experience in deploying banking analytics use cases, including credit risk, fraud detection, AML/BSA, customer segmentation, and marketing optimization.
• Familiarity with regulatory frameworks such as SR 11-7, CCAR, and CECL.
• Experience working in an Azure-first cloud environment at scale within financial services.
• Exposure to Generative AI / LLM applications, including use cases such as document intelligence, customer interaction, or internal productivity tools.
• Strong understanding of data governance, privacy, and regulatory compliance frameworks in banking.
• Demonstrated ability to build, scale, and retain high-performing data science teams.

Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.

The base pay range for this position is USD $175,000.00/Yr. - USD $275,000.00/Yr. Exact offers will be determined based on job-related knowledge, skills, experience, and location.