Job Description

Job Description:

This analyst plays a critical role by turning complex operational data into clear insights that streamline QA processes, rationalize inventories, and support the adoption of AI-enabled quality solutions. This is not a technical AI development role—the person does not need to understand how AI models work internally. Instead, success depends on strong analytical judgment, business sense, and the ability to evaluate AI-generated outputs, run structured test scenarios, and determine what is reliable, repeatable, and ready for use.

 Analytics, Experimentation & Decision Support

 Run structured test scenarios against QA and inventory datasets; interpret results and provide recommendations—no AI modeling experience needed.

Translate metadata-rich datasets (e.g., ~2,500 QA reviews with many fields) into clear patterns, insights, and prioritization recommendations.

Identify the components of data that truly drive outcomes and determine which items should move toward automation.

Develop concise, insight-rich scenario analyses that guide decisions around efficiency, risk, and operational improvements.

 AI-Enabled QA & Output Evaluation

(No AI technical expertise or model-building skills required)

 Partner with technical teams to evaluate outputs from AI/LLM models that support or replace manual QA activities.

Use strong analytical thinking to determine whether AI results make business sense, follow criteria, and meet quality thresholds.

Create and refine prompting strategies—not by understanding AI engineering, but by testing inputs and observing outcomes to learn what works.

Apply business judgment to escalate issues, recommend corrections, or determine when human oversight is needed.

 Inventory Rationalization & Standardization

Rationalize QA inventory by consolidating duplicative activities, standardizing taxonomies, and removing unnecessary variation.

Identify pattern similarity across QA reviews to streamline processes and expose automation opportunities.

Incorporate metadata and business rules into QA evaluations to enhance precision and consistency.

Stakeholder Partnership & Operating Model Support

Work closely with business, operations, product, and engineering teams to validate inputs, interpret findings, and drive action.

Support governance forums with concise, decision-focused insights rather than large reporting packages.

Encourage consistent adoption of standardized processes, lifecycle expectations, and change management practices.

 Data Quality, Controls & Tooling

Ensure data accuracy, consistency, and traceability across QA and inventory datasets.

Use Advanced Excel and SAS to clean data, run comparisons, build scenarios, and generate insights.

Partner with technology teams to enhance telemetry and create more accessible analytics pipelines.

Required Qualifications

• 5+ years in analytics, business analysis, quality/controls, PMO analytics, or similar fields.
• Ability to analyze structured datasets and synthesize insights for operational and executive audiences.
• Strong analytical judgment and problem-solving skills; able to determine when outputs “make sense.”
• Hands-on expertise with Advanced Excel and SAS for data manipulation and scenario modeling.
• Excellent communication and business reasoning skills—able to translate data into decisions.
• Comfort evaluating AI outputs, even without understanding AI technically.

Desired Qualifications

• Experience in banking or business consulting; an understanding of how operations and controls function.
• Exposure to AI/LLM-enabled work is helpful but not required; willingness to learn through structured testing is sufficient.
• Background in quality, manufacturing-style process discipline, or pattern classification.
• Familiarity with governance routines, standardization efforts, and risk/control frameworks.
• Experience identifying similarities in activities to support automation and process rationalization.

Tools & Technologies

• Advanced Excel (required)
• SAS (required)

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