Responsibilities span the following areas:

#### Analytics engineering & automation

• Design and build data pipelines that connect to source systems, transform raw data into audit-ready datasets, and enable full-population testing across entities and jurisdictions.


• Develop automated test scripts, anomaly detection models, and risk scoring mechanisms that replace manual sampling with continuous, data-driven assurance.


• Validate the completeness and accuracy of data used in analytics — you don’t just visualize data, you first prove it matches the source system.


• Leverage AI-assisted tools to enhance analytics workflows, from accelerating data extraction and transformation to building smarter detection models.


• Partner with SOX testers and operational auditors to translate audit objectives into scalable analytical tests.


• Identify opportunities to leverage emerging technologies, including AI-assisted tools, to enhance coverage and efficiency.

#### Visualization & continuous monitoring

• Build and maintain dashboards and visualizations that provide Internal Audit and senior leadership with real-time insights into control effectiveness, exception trends, testing coverage, and audit findings.


• Design continuous monitoring solutions that shift Internal Audit from periodic, point-in-time testing to ongoing surveillance of control health across critical processes.

#### Stakeholder engagement & analytics enablement

• Work with Engineering, Data, and Business Operations teams to identify data sources, understand data lineage, and secure timely access to the data needed for audit analytics.


• Champion a data-first mindset within Internal Audit. Train auditors on analytics tools, build self-service datasets and documentation, and empower non-technical team members to explore data independently.


• Communicate complex data analytical findings clearly to both technical and non-technical audiences, translating data into actionable audit conclusions for senior leadership and the Audit Committee.

Skills you should HODL

• 8+ years of experience in data analytics, data science, analytics engineering, or data engineering, with proven application in an audit, risk, or financial services context.
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Information Systems, or a related quantitative field.
• Advanced proficiency in SQL and Python. Experience building data visualizations and dashboards using tools such as Tableau, Power BI, or equivalent.
• Rigorous approach to data quality, including completeness, accuracy, and reconciliation to source systems is second nature, not an afterthought.
• Comfortable integrating AI-assisted tools into analytics workflows. You don't need to be an AI engineer, but you should know how to use AI to work faster and smarter.
• Demonstrated ability to translate ambiguous audit or business questions into structured analytical approaches and scalable solutions.
• Effective communicator who can present complex data findings as clear, actionable conclusions for auditors, senior leadership, and external stakeholders.

Nice to haves

• Professional certifications: CIA, CISA, CPA, CAP (Certified Analytics Professional), or cloud data certifications (AWS, GCP).
• Experience building continuous auditing or continuous monitoring programs, including cloud data infrastructure such as Snowflake, AWS, or GCP.
• Experience using AI/ML tools for anomaly detection, data extraction, or workflow automation in an audit or analytics context.
• Familiarity with crypto, fintech, payments, or digital asset environments.