Mgr, Retailer Data Exchange & Benchmark Research

Numerator is seeking a Manager of Retailer Data & Benchmark Research to lead the development, validation, and ongoing optimization of retailer-level benchmarks and external calibration signals that power our panel data products. This role sits at the intersection of data science, analytics, and product, and is critical to ensuring our data accurately reflects real-world retail dynamics.

You will be responsible for the continued evolution of how we incorporate external signals into our ecosystem, evaluating data quality, and translating complex data behaviors into clear, actionable insights for internal teams and clients.

• Own the design, evolution, and performance of retailer-level benchmarks used in calibration.

• Identify and integrate external data sources (e.g., POS, syndicated data, retailer signals).

• Partner with Data Science to refine calibration methodologies and ensure benchmarks reflect channel, retailer, and shopper differences.

• Define and monitor key data quality and benchmark performance metrics, including drift.

• Lead analysis of discrepancies between panel data and external benchmarks, identifying and articulating root causes.

• Develop scalable frameworks to distinguish expected variability from true data issues and operationalize AI-enabled investigative workflows.

• Conduct deep-dive analyses of retailer and category trends to inform product and calibration improvements.

• Translate complex data behaviors into clear insights for internal stakeholders and clients.

• Establish guidelines for interpreting meaningful vs. immaterial data movement.

• Partner with Product, Engineering, Data Science, Operations, and Client teams to align priorities and operationalize solutions.

• Support go-to-market efforts by articulating data strengths, limitations, and appropriate use cases.

• Serve as a subject matter expert on retailer data, benchmarks, and calibration strategy.

• Build scalable processes, dashboards, and reporting to improve visibility into data quality and benchmark performance.

• Drive continuous improvement in calibration and validation processes.

• Stay current on GenAI capabilities and translate emerging technologies into practical applications for data quality and analytics.