Internal Audit Data Analyst Supervisor

Position Overview

onsemi is seeking a Data Analyst Supervisor to join our Internal Audit responsible for designing, building, and scaling AI‑enabled analytics and automation solutions that modernize audit execution. The Data Analyst Supervisor operates hands‑on across the full lifecycle—from problem framing to deployment—while shaping Internal Audit’s AI and automation direction through practical delivery, demonstrated value, and influence

Key Responsibilities

Data & Analytics: Design and execute audit analytics (trend, anomaly, outlier, process, and root-cause analysis) using tools such as SQL, Python (pandas/numpy/matplotlib), Power BI/Power Query, and enterprise data platforms (e.g., Snowflake). Build and maintain Internal Audit datasets/data models (e.g., curated tables, semantic models, metric definitions) that enable repeatable testing and continuous assurance. Develop robust data preparation and validation routines (reconciliations, completeness checks, lineage documentation, exception handling) to ensure analytics are audit-defensible and reproducible.

AI-Enabled Analytics & Automation: Develop and apply GenAI-assisted audit workflows (e.g., Copilot M365, Copilot Studio) to accelerate analysis, documentation, and insight generation—while ensuring appropriate controls around data handling, confidentiality, and human-in-the-loop validation. Implement AI-enabled techniques to improve speed and quality of audit work (e.g., clustering, text classification, issue pattern detection, intelligent sampling, variance explanations) where appropriate and feasible. Create reusable prompts, templates, and operating guidance that help audit teams use AI tools responsibly and consistently.

Automation & Engineering Mindset: Build and maintain repeatable audit routines and automated testing using scripting and low-code automation (e.g., Python, Power Automate, Power Apps) and DevOps practices (e.g., Azure DevOps/GitHub).

Continuous Auditing & Monitoring: Partner with auditors to identify high-value opportunities for continuous monitoring/controls testing and implement scalable routines (scheduled refresh, exception alerts, audit dashboards). Develop dashboards and reporting that provide timely visibility into risks, trends, and exceptions, enabling continuous risk sensing and stronger audit planning

Stakeholder Engagement & Storytelling: Partner with audit leadership, audit teams, business stakeholders, and enabling IT teams to understand processes, systems, and data lineage—translating complexity into clear, actionable insights. Present analysis results in business-friendly narratives (impact, root cause, recommendation, and priority) and support audit teams with evidence packages that are clear and defensible. Contribute to Internal Audit’s evolving analytics/automation approach by proposing practical use cases and standards grounded in delivery experience (not formal program ownership).

Peer Enablement & Knowledge Sharing: Serve as a hands-on subject matter expert by creating and maintaining audit analytics playbooks, reusable test libraries, prompt guidance, and documentation standards. Enable adoption through demos, office hours, and “how-to” documentation—supporting upskilling across the audit team without direct management responsibility. Promote responsible AI practices (prompt hygiene, data minimization, secure handling, validation, and auditability) aligned to risk and compliance expectations.

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Qualifications

• Bachelor’s degree in MIS, Computer Science, Data/Business Analytics, or related field (or equivalent practical experience).
• 10+ Years of experience delivering analytics and automation solutions, ideally in complex, multinational environments (e.g., U.S. public company context).
• Strong hands-on capability in: 
• SQL and relational data concepts
• Python for analysis and automation (pandas/numpy; scripting for repeatable routines)
• Power BI / Power Query for dashboards, modeling, and self-service analytics
• Experience working with data platforms (e.g., Snowflake) and building curated, audit-ready datasets.
• Demonstrated ability to operate as a senior individual contributor: independently driving delivery, communicating clearly, and influencing outcomes through expertise rather than formal authority.
• Strong written and verbal communication skills; ability to translate technical findings into business impact and recommendations.

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