VP, Risk Data Analytics Manager

Cathay Bank•Los Angeles, CA

23h•$120,000 - $152,000

About The Position

The Risk Data Analytics Manager is an independent contributor within the Operational Risk (second line) function, reporting to the SVP, Director of Operational Risk Management. This role ensures accurate collection, aggregation, and governance of risk data across the enterprise, delivering trusted metrics and narratives that support risk identification, assessment, monitoring, and reporting under the Bank’s ERM/ORM framework. The incumbent acts as the data and analytics expert for operational risk, partnering with various risk disciplines and business lines to provide timely, auditable insights, maintain data quality and lineage, and enable regulator-facing disclosures, while upholding independence, objectivity, and data security. The role bridges risk management expertise and data analytics capability, ensuring that the Bank’s ORM and ERM frameworks are supported by accurate risk metrics and actionable insights.

Requirements

Bachelor’s or Master’s degree in Data Science, Statistics, Risk Management, Finance, Economics or related field.
Minimum 5-7 years of experience in data analytics, risk data management of Data Governance in financial services; prior exposure to operational risk or ERM is a plus.
Experience with data visualization tools (Power BI, Tableau) and Governance, Risk and Compliance (GRC) systems.
Understanding of operational risk management principles, frameworks, and methodologies within the financial services industry. This includes knowledge of regulatory requirements (such as Basel III) and industry best practices.
Strong analytical mindset with ability to translate data into actionable risk insights and clear narratives.
Strong proficiency in SQL, Python.
Collaborative mindset with a proactive attitude towards problem-solving.
Clear written and verbal communication; ability to produce concise risk reporting narratives for diverse audiences.

Responsibilities

Aggregate risk data from multiple sources (risk databases, incident/loss databases, control libraries, identification tools, third-party data) into a single, auditable data set.
Implement and monitor data quality controls (completeness, accuracy, timeliness, consistency) and track remediation activities. Maintain data lineage, metadata, and data dictionaries; coordinate with data owners to resolve data issues.
Design, develop and maintain dashboards and reports for ORM and ERM metrics (using tools such as Power Bi).
Define, document, and maintain core operational risk metrics (e.g., risk IDs, loss distributions, RCSA drivers, KRIs, KPI dashboards, control effectiveness scores). Ensure metrics align with ERM/MRM frameworks and regulatory expectations; update metrics as risk taxonomy evolves. Validate metrics against source data, perform back-testing, and document assumptions and limitations.
Use statistical techniques/advanced analytics (e.g., correlation, clustering, regression and scenario modeling) to enhance understanding of risk drivers and systemic vulnerabilities.
Support enterprise risk identification and risk assessment processes by delivering accurate data extracts, trend analyses, and thematic insights. Monitor risk indicators and control effectiveness metrics; flag data-driven anomalies for review.
Produce concise periodic reports to support risk committees and governance. Prepare clear narratives and data stories that explain risk trends, drivers, and remediation progress.
Assist in regulatory reporting by providing accurate data and supporting documentation.
Develop trend analyses, heat maps and scenario analytics to identify top and emerging risks and control weaknesses.
Assists in quantifying and visualizing top risk exposures across risk types (credit, market, operational, compliance, strategic, etc.).
Conduct deep-dive analysis on key operational and enterprise risk themes to uncover trends, root causes, and control gaps across business lines.
Analyze operational loss data, RCSA results, and issue management to identify risk patterns.
Identify opportunities to automate manual risk reporting and improve efficiency.
Participate in GRC system enhancement projects and user acceptance testing for risks systems.