Sr. Quality Analytics Consultant

Sr. Quality Analytics Consultant Duration- 12 Months Pay-$60-$65/Hr Hybrid- 3 days in office/2 remote Location- Charlotte, NC Role Summary Short, Direct Job Description This role translates complex QA and inventory data into clear, actionable insights that improve efficiency, reduce manual work, and support the adoption of AI-enabled quality solutions. The analyst evaluates AI-generated outputs using business judgment—not AI engineering—runs structured test scenarios, and determines what is reliable, repeatable, and ready for automation. Success depends on strong analytical thinking, operational understanding, and the ability to turn data into decisions. Key Responsibilities Analytics & Decision Support • Analyze QA and inventory datasets to identify patterns, risks, and efficiency opportunities. • Run structured test scenarios and deliver concise, decision-focused insights. • Identify data elements that drive outcomes and should move toward automation. AI-Enabled QA Evaluation • Evaluate AI/LLM-generated QA outputs for accuracy, consistency, and business validity. • Test and refine prompting approaches through structured experimentation. • Recommend when AI is production-ready and when human oversight is required. Inventory Rationalization & Standardization • Consolidate duplicative QA activities and standardize taxonomies and business rules. • Identify similarities across QA reviews to streamline processes and enable automation. Stakeholder Partnership & Governance • Partner with business, operations, product, and technology teams to drive action. • Support governance forums with clear, concise insights rather than heavy reporting. Data Quality & Tooling • Ensure data accuracy, consistency, and traceability. • Use Advanced Excel and SAS to clean data, model scenarios, and generate insights. Required Qualifications • 5+ years of experience in analytics, business analysis, quality/controls, or consulting. • Strong analytical judgment and ability to assess whether results “make sense.” • Advanced Excel and SAS expertise. • Ability to translate complex data into clear business recommendations. • Comfort evaluating AI outputs without technical AI or model-building knowledge. What Success Looks Like • A streamlined, standardized QA inventory with reduced manual effort. • Clear, repeatable methods for evaluating AI-enabled QA outputs. • Faster, higher-quality decisions driven by focused insights. • Measurable efficiency gains through rationalization and targeted automation. If you want, I can also: • Tune the title for banking vs consulting vs internal enterprise • Cut this down further into a one-page posting • Reword it to match a specific company style (Wells, JPM, consulting firms, etc.)