Data Analytics Project Manager

Huntington National Bank•Columbus, OH

3d•Onsite

About The Position

The Data Analytics Project Manager will help advance Internal Audit’s use of data, automation, and emerging technology. The role partners with audit teams to design and execute data-driven audit solutions, including population-based testing, exception analysis, continuous auditing, and other advanced analytics approaches. It supports multiple businesses and functions and regularly engages with Audit, Compliance, Risk Management, Technology, Finance, and business-area stakeholders. Alongside delivering analytics projects, the Project Manager develops repeatable, scalable, and reusable analytics capabilities to improve audit coverage, quality, efficiency, and insight. The position also provides informal coaching and development to team members, reviews analytical work and related documentation, communicates project status and results to stakeholders, and contributes to the continued evolution of Internal Audit’s analytics capabilities.

Requirements

Bachelor’s degree in Business Analytics, Data Analytics, Data Science, Computer Science, Management Information Systems, Accounting, Finance, or a related discipline.
At least 4 years of experience in data analytics, audit analytics, technology, risk management, internal audit, or a related field.

Nice To Haves

Experience translating business, risk, or audit objectives into data-driven solutions.
Experience performing or supporting data validation, analytical testing, or population-based analysis.
Strong analytical, critical-thinking, problem-solving, and communication skills.
Demonstrated experience using SQL to analyze large and complex datasets.
Experience in Internal Audit, Risk Management, Compliance, banking, financial services, or another highly regulated industry.
Experience developing population-based, exception-based, or continuous analytics.
Experience with Python or other data analysis or scripting languages.
Experience with business intelligence and visualization tools such as Tableau.
Experience with automation, artificial intelligence, or emerging analytics technologies.
Experience designing repeatable or reusable analytical solutions.
Understanding of data quality, lineage, governance, and validation concepts.
Knowledge of banking processes, risks, regulations, and regulatory expectations.
Strong project management and stakeholder-management skills.
Ability to clearly communicate complex analytical concepts to nontechnical audiences.
Ability to manage multiple priorities and work effectively both independently and collaboratively.
Strong curiosity and demonstrated ability to learn new technologies and analytical techniques.

Responsibilities

Lead the design, development, execution, and implementation of data analytics solutions for audit planning, testing, continuous auditing, and risk identification.
Partner with audit teams during planning to identify opportunities for full-population testing, exception-based analysis, continuous auditing, automation, and other data-driven audit approaches.
Develop and enhance reusable and scalable analytics solutions to reduce reliance on one-time analysis and improve consistency across audit engagements.
Develop, maintain, and improve continuous auditing solutions, including recurring population analysis, exception identification, trend analysis, and delivery of testing results.
Evaluate data completeness, accuracy, lineage, transformation logic, and fitness for audit purposes; document analytics work to support audit reliance.
Translate audit objectives, risks, and business problems into practical data and technology solutions.
Analyze large and complex datasets to identify exceptions, anomalies, patterns, trends, and other insights relevant to audit objectives.
Identify appropriate opportunities to use automation, artificial intelligence, and emerging analytics capabilities to improve audit effectiveness and efficiency.
Ensure analytics activities and supporting documentation meet departmental and professional standards and established timelines.
Review analytical work performed by others and provide coaching, technical guidance, and quality review.
Build strong relationships with Audit, Technology, Risk, Compliance, Finance, and business partners.
Stay current on developments in data analytics, automation, AI, visualization, and audit technology, and evaluate practical applications within Internal Audit.