Location: Charlotte, NC
Salary: $53.00 USD Hourly - $57.00 USD Hourly
Description: Senior Business Analytics ConsultantLocation: Charlotte, NC (Hybrid - 3 days onsite)
Contract: 12 months, with strong potential to extend or convert to FTE
Start: ASAP
About the RoleWe are seeking a Senior Business Analytics Consultant to transform complex operational datasets into actionable insights that drive quality assurance (QA) excellence, inventory rationalization, and the adoption of AI-enabled quality solutions.
This is
not a technical AI development role. Instead, success depends on analytical judgment, structured testing, and the ability to evaluate AI-generated outputs for accuracy, consistency, and business fit.
You will partner closely with operations, product, engineering, and governance teams to streamline QA processes, standardize inventory, and support an evolving AI-assisted operating model.
What You'll DoAnalytics, Experimentation & Decision Support- Execute structured test scenarios using QA and inventory datasets; interpret findings and provide clear recommendations.
- Analyze large, metadata-rich datasets to identify trends, priority areas, and operational improvement opportunities.
- Determine which QA components or steps are suitable for automation based on data-driven patterns.
- Produce concise, scenario-based insights that support decisions around efficiency, risk mitigation, and process improvements.
AI-Enabled QA Evaluation(No AI engineering or model-building experience required)- Partner with technical teams to evaluate outputs from AI/LLM tools that augment or replace manual QA tasks.
- Assess AI-generated results to ensure they meet business logic, criteria adherence, and quality thresholds.
- Refine prompting strategies by experimenting with inputs and learning from observed outcomes.
- Recommend corrections, highlight inconsistencies, and determine where human oversight remains essential.
Inventory Rationalization & Standardization- Streamline QA inventories by eliminating duplicative processes and reducing unnecessary variation.
- Standardize taxonomies and business rules to improve QA consistency and accuracy.
- Identify similarity patterns across QA activities to uncover automation opportunities and operational efficiencies.
Stakeholder Partnership & Operating Model Support- Collaborate with business, operations, product, and engineering teams to validate data inputs, interpret insights, and drive action.
- Provide crisp, decision-oriented insights for governance forums-without heavy reporting packages.
- Promote adoption of standardized processes, lifecycle expectations, and change-management best practices.
Data Quality, Controls & Tooling- Ensure data accuracy, consistency, and traceability across QA and inventory datasets.
- Use Advanced Excel and SAS to clean data, compare scenarios, and build analytical models.
- Partner with technology teams to enhance telemetry and establish scalable analytics pipelines.
Tools & TechnologiesRequired:Nice to Have:- SQL
- Python
- Visualization tools (e.g., Tableau, Power BI)
What Success Looks Like- A more consistent, simpler QA inventory with fewer variances and cleaner standards.
- Repeatable test harnesses for evaluating AI/LLM outputs using business criteria.
- Faster, more informed decision-making through concise, insight-forward analytics.
- Reduced manual QA effort enabled by rationalization and targeted automation.
Required Qualifications- 5+ years in analytics, business analysis, QA/controls, PMO analytics, or a related field (or comparable experience through consulting, military service, or education).
- Proven ability to analyze structured datasets and synthesize insights for operational and executive audiences.
- Strong analytical judgment and business reasoning-comfortable assessing whether outputs "make sense."
- Hands-on expertise with Advanced Excel and SAS for data manipulation, comparisons, and scenario modeling.
- Excellent communication skills with the ability to translate data into clear decisions.
- Comfort evaluating AI-generated outputs (no technical AI expertise needed).
Desired Qualifications- Experience in banking, operations, or business consulting, especially in quality/control environments.
- Exposure to AI/LLM-enabled workflows or willingness to learn through structured testing.
- Background in quality management, process discipline, or pattern classification.
- Familiarity with governance routines, standardization programs, and risk/control frameworks.
- Experience identifying pattern similarities to support automation and process optimization.
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