AI Business & Insights, Senior Analyst

Location:

4910 Tiedeman Road, Brooklyn Ohio

The Performance & Insights Senior Analyst is responsible for measuring business performance and surfacing actionable quantitative and qualitative insights that inform leadership decisions, improve effectiveness, and drive better outcomes. This role goes beyond reporting—connecting performance data, employee sentiment, and feedback loops to assess progress against expectations and identify opportunities to improve impact, adoption, and results.

This individual serves as a thought partner to leaders, translating data into insight, insight into action, and action into measurable improvement. The role also plays a critical enablement function—reinforcing data standards and practices that ensure insights are scalable, governed, and ready to support automation and AI-driven capabilities.

• Define, govern and maintain performance frameworks that measure progress against strategic goals, commitments, and expected outcomes, ensuring consistency, comparability and credibility across business areas.
• Establish and manage KPIs, success metrics, and scorecards that reflect outcomes and effectiveness—not just activity.
• Assess performance trends, gaps, and risks, clearly articulating what is working, what is not, and why.

• Synthesize quantitative data (metrics, dashboards, operational performance) with qualitative inputs (employee feedback, sentiment, observations) to deliver holistic insights.
• Identify leading and lagging indicators that predict performance outcomes and business impact.
• Translate complex data into clear, executive-ready narratives that inform decision making.

• Design and manage feedback mechanisms (surveys, pulse checks, listening posts, qualitative interviews) to capture employee sentiment and experience.
• Analyze themes, patterns, and sentiment trends to understand engagement, friction points, and change readiness.
• Partner with leaders to close feedback loops—ensuring insights result in visible action and measurable improvement.

• Evaluate the effectiveness of initiatives, operating models, and ways of working against intended outcomes.
• Identify opportunities to improve performance, adoption, efficiency, and value realization.
• Support experimentation and continuous improvement by defining success criteria and measuring impact over time.

• Serve as a connector between AI enablement, analytics, data governance, and business teams to ensure AI insights are scalable, governed, and reusable.
• Support the transition from AI experimentation to production-ready, business-aligned capabilities by reinforcing standards, expectations, and performance implications.
• Encourage use of governed, standardized data assets to support AI models and automation—highlighting where unmanaged data limits scale or increases risk.
• Influence data and insight standards to ensure performance intelligence is production-ready, auditable and fit for AI-driven decision support.
• Use performance and adoption insights to surface AI-related governance gaps, duplication, or unmanaged experimentation that create risk or inefficiency.

• Act as a trusted advisor to business and functional leaders, providing objective insight and data-driven recommendations.
• Influence prioritization and decision making by grounding discussions in evidence, outcomes, and tradeoffs.
• Collaborate across teams (analytics, operations, change, communications) to align insights, performance narratives, and adoption strategies.

• Leaders have clear, trusted visibility into performance and effectiveness.
• Decisions are grounded in both data and employee insight.
• Feedback loops are closed, and employee sentiment informs real change.
• Insights are timely, actionable, and built on governed, reusable data foundations.
• Business outcomes improve—and those improvements are measurable, scalable, and understood.

• 5 years of experience in business…