Specialist - Corporate Audit

Company Profile

Lennox (NYSE: LII) Driven by 130 years of legacy, HVAC and refrigeration success, Lennox provides our residential and commercial customers with industry-leading climate-control solutions. At Lennox, we win as a team, aiming for excellence and delivering innovative, sustainable products and services. Our culture guides us and creates a workplace where all employees feel heard and welcomed. Lennox is a global community that values each team member’s contributions and offers a supportive environment for career development. Come, stay, and grow with us.

Job Description Role Objective The Data Analytics Specialist will support the Corporate Audit function by developing and executing data analytics solutions to enable risk-based audits, SOX compliance, fraud detection, continuous auditing, and ERM initiatives. The role will focus on

leveraging analytics-related tools (e.g., IDEA, Alteryx, Azure) and

SAP data to deliver actionable insights and scalable audit analytics.

Key Responsibilities Audit Data Analytics & Automation

• Design, develop, and maintain models and/or workflows for audit analytics, fraud detection, SOX testing, and continuous auditing.
• Document the processes and procedures for models or workflows developed.
• Build, document, and maintain an inventory of audit analytics and use cases across business processes (e.g., P2P, O2C, Inventory, GL, etc.).
• Automate repetitive audit testing procedures to improve audit coverage and efficiency (e.g., test populations versus samples).
• Perform risk-based data analysis, including exception testing, trend analysis, and anomaly detection.

Risk Monitoring, Fraud & ERM Support

• Support fraud desk initiatives through advanced analytics and red flag identification using structured SAP data.
• Enable risk response monitoring aligned with ERM and Internal Audit priorities.
• Develop analytics to support management’s proactive risk identification and control effectiveness assessment.

Technology & Data Enablement

• Work directly with SAP source data (e.g., FI, MM, SD, Inventory modules) and ensure accurate data extraction and transformation.
• Leverage Azure or similar data platforms for large-scale risk analysis and enterprise analytics.
• Address data sourcing limitations and improve data reliability for audit consumption.
• Act as a subject-matter expert for analytics tools (e.g., IDEA and Alteryx), including workflow optimization and complexity review.

Stakeholder Collaboration

• Partner with Internal Audit, Compliance, IT, Finance, and Supply Chain teams to understand risk areas and data availability.
• Provide clear visibility on analytics deliverables, scope, and outcomes to audit leadership.
• Support follow-up discussions with stakeholders on analytics-driven observations.

Governance & Capability Building

• Define standards and governance for audit analytics development and documentation.
• Review and validate complex models and workflows developed by the team to ensure quality and sustainability.
• Support training and mentoring of auditors on the use and interpretation of analytics.

Qualifications Required Qualifications

• Bachelor’s degree in data Analytics, Information Systems, Finance, Engineering, or related field.
• 4–7 years of experience in Data Analytics, Business Intelligence, Internal Audit, or Risk Analytics.
• Deep, hands-on experience with modeling and advanced workflow design and review capability.
• Strong working knowledge of SAP data structures and audit-relevant datasets.
• Experience supporting Internal Audit, SOX, fraud analytics, or risk monitoring initiatives.

Preferred Skills & Experience

• Business Intelligence background with strong analytics mindset.
• Experience with enterprise data lake environments.
• Knowledge of audit methodologies, internal controls, and compliance frameworks.
• Ability to translate audit objectives into scalable analytics solutions.

Key Success Factors

• Strong ownership of end-to-end analytics delivery.
• Ability to work independently in a developing audit analytics environment.
• Practical problem-solving approach to data availability and quality issues.
• Clear communication with audit leadership and business stakeholders.