Analytics Consultant 4 - Contingent
Location: Charlotte NC (Hybrid)
Duration: 12+ months
Job ID: 174189
Job Description:
In this contingent resource assignment, candidate may:
- Consult on complex initiatives with broad impact and large-scale planning for Analytics.
- Review and analyze complex multi-faceted, larger scale or longer-term Analytics challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors.
- Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables.
- Strategically collaborate and consult with client personnel.
- 5 plus years of Analytics experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
- 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 and 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.
- (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.
- 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.
- 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.
- 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.
- 5 plus 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.
- 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.
- Advanced Excel (required)
- SAS (required)
- Optional: SQL, Python, or visualization tools for exploratory analysis.
If you receive a suspicious message, email, or phone call claiming to be from PTR Global do not respond or click on any links. Instead, contact us directly at +1 214-740-2424. To report any concerns, please email us at [email protected]
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