Analytics Consultant

Location: Charlotte, NC
Salary: $53.00 USD Hourly - $57.00 USD Hourly
Description:
Senior Business Analytics Consultant

Location: Charlotte, NC (Hybrid - 3 days onsite)
Contract: 12 months, with strong potential to extend or convert to FTE
Start: ASAP

About the Role

We 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 Do
Analytics, 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 & Technologies

Required:

• Advanced Excel
• SAS

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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