Retail Data & Benchmark Lead — Insights & Calibration

Numerator; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Volunteer time off

Donation matching

Career growth support

Mentorship programs

Job summary

Numerator seeks a Manager of Retailer Data & Benchmark Research to lead the development and validation of retailer-level benchmarks powering our panel data products. This role sits at the intersection of data science, analytics, and product, translating complex data into actionable insights for internal teams and clients.

You will collaborate with Product, Engineering, Data Science, Operations, and Client teams to refine calibration methods, monitor data quality, and drive AI-enabled workflows

Qualifications

• 7+ years in Data Science, Analytics, or related field with leadership experience.
• Experience with panel data, POS data, or large-scale consumer datasets.
• Familiarity with calibration, benchmarking, or statistical modeling concepts.

Responsibilities

• Lead design, evolution, and performance of retailer-level benchmarks used in calibration.
• Identify and integrate external data sources (POS, syndicated data, retailer signals).
• Partner with Data Science to refine methodologies and reflect channel/retailer/shopper differences.
• Define data quality metrics and monitor benchmark performance including drift.
• Translate data behaviors into clear insights for internal teams and clients.

Skills

Benchmarking

Data quality

Leadership

SQL

Python

Education

Quantitative degree (BS/MS/PhD)

Tools

SQL

Python

Job description

Numerator seeks a Manager of Retailer Data & Benchmark Research to lead the development and validation of retailer-level benchmarks powering our panel data products. This role sits at the intersection of data science, analytics, and product, translating complex data into actionable insights for internal teams and clients.

You will collaborate with Product, Engineering, Data Science, Operations, and Client teams to refine calibration methods, monitor data quality, and drive AI-enabled workflows