Data Analytics

Mizuho•New York, NY

1d•$127,000 - $210,000•Hybrid

About The Position

The Internal Audit Department Americas (IADA) provides internal audit services to the branches, representative offices and agencies of Mizuho Bank, Ltd. in the Americas, and to Mizuho Bank (USA). IADA’s mission is to act as an independent, objective assurance and consulting function, designed to add value and improve Mizuho Bank’s U.S. operations including the derivatives and broker dealer businesses. Based in the New York Metro area, IADA staff members perform various audits of different business areas of the bank to evaluate the effectiveness of risk management and governance processes. Along with its counterparts in London, Hong Kong and Singapore, IADA reports to and composes the overseas arm of MHBK’s Internal Audit Division (IAD). Data Analytics Function’s Coverage Summary Execute the Group’s IADA Data Analytics program, including fulfilling the day-to-day analytics needs of the IADA teams as well identifying additional opportunities to leverage data analytics to optimize internal audit quality, coverage and cost. This individual will identify, design, develop, and maintain the data analytics routines needed to support audit activities including audit execution, continuous monitoring, and department level management and reporting.

Requirements

Bachelor’s Degree or equivalent in business, mathematics, computer science, management information systems, or a related field.
Significant experience (10-15+ years) in internal audit, external audit, regulatory examination, or risk management within financial services, including demonstrated experience leading teams or workstreams.
Strong hands-on expertise in audit data analytics, including full-population testing, anomaly detection, trend analysis, and analytics-enabled control testing.
Demonstrates strong knowledge and proficiency in data extraction, transformation, and analytics tools, with demonstrated experience leveraging a wide variety of data analysis tools:
Database Management: Oracle, Microsoft SQL Server, Azure SQL, Snowflake, Sybase
Analytics Tools: SQL, Python, PySpark, SparkSQL
Reporting and Visualization Tools: PowerBI, Tableau, QlikView
Data Integration and ETL Tools: Azure (Azure Data Factory, ADLS), Databricks, Airflow, SQL Server Integration Services (SSIS)
Data Automation and Scheduling Tools: Tidal
Experience implementing and operating continuous auditing and monitoring programs, including automation of recurring tests and exception reporting.
Solid understanding of audit methodology, internal controls, and audit documentation standards, with the ability to integrate analytics into audit workpapers and conclusions.
Experience in banking, capital markets, treasury, risk management (credit, market, liquidity and operational), audit, finance with knowledge of risks and controls within Capital Markets.
Strong communication skills, both written and verbal, with the ability to explain analytics results clearly to auditors, management, and non-technical stakeholders.
Demonstrated people-management capabilities, including coaching staff, reviewing work quality, managing performance, and supporting talent development.
Effective project and executing management skills, with the ability to prioritize competing demands, manage multiple initiatives, and deliver results within required timelines.

Nice To Haves

Advanced degree and/or professional certifications (e.g., MBA, CPA, CIA, CISA, CFE, or equivalent) preferred.
Experience applying or piloting such techniques in an audit, risk, or analytics context is a plus but not required.

Responsibilities

Under the direction of the Head of Data Analytics, lead the execution of the IADA Data Analytics program.
Translate strategic objectives into executable plans, deliverables, and timelines across audit execution, continuous monitoring, and departmental reporting.
Design, develop, and maintain audit analytics routines that support audit planning, executions, and continuous auditing.
Ensure analytics enable increased testing coverage, full-population testing, and identification of outliers, anomalies, patterns, and trends to evaluate control design and operating effectiveness.
Drive operational effectiveness and consistency of analytics usage across audits by establishing standard methodologies, reusable analytics assets, and execution playbooks aligned with Internal Audit standards and regulatory expectations.
Oversee data extraction, transformation, and preparation activities to support audit analytics, working closely with Technology and Business partners to obtain reliable, timely, and well-controlled data inputs.
Manage and enhance analytics infrastructure and tools used by the IADA team, ensuring solutions are scalable, well-documented, and fit for audit use.
Develop and implement automated testing and continuous monitoring solutions under the direction of the Head of Data Analytics, expanding the use of analytics to improve audit efficiency, coverage, and timeliness.
Act as a primary point of contact for audit teams and business partners on analytics-related matters, including data sourcing, interpretation of results, and integration of analytics into audit programs.
Provide technical leadership and quality review over analytics-supported audit testing, including reviewing workpapers, validating methodologies, assessing test results, and supporting conclusions regarding control design and operating effectiveness.
Lead execution of complex analytics initiatives and special projects, including regulatory-driven analytics, enterprise data efforts, and cross-audit analytics programs, as assigned by the Head of Data Analytics.
Track, analyze, and report on program-level metrics related to analytics adoption, effectiveness, and efficiency.
Prepare management-level dashboards and narratives to support Internal Audit leadership reporting.
Direct, coach, and develop IADA Data Analytics staff, including assigning work, reviewing performance, providing feedback, and supporting ongoing professional development for junior staff.
Foundational understanding AI and advanced analytics concepts, including exposure to machine learning, generative AI, and automation capabilities, and an understanding of how such tools may be applied within Internal Audit.