Data Scientist

Location: Chicago (hybrid) preferred, remote available

Be a part of a fast-growing, winning team helping Fortune 1000 consumer brands and retailers leverage AI-driven data insights.

Open Brand is one of the world’s most respected market intelligence companies. Open Brand’s data and market research products give manufacturers, retailers, and industry players a competitive edge across a wide range of industries (including IT, consumer electronics, home appliances, health, wellness, beauty, small appliances, and other consumer durables) and help marketing, product, sales, and pricing teams make more informed decisions in a rapidly changing market environment.

This role is a hands-on Data Scientist focused on building, scaling, and maintaining production-grade market insight, market share, media spend estimation, and channel sizing products powered by alternative datasets such as email receipts, clickstream, credit card transaction data, ad/media signals, and external benchmarks.

You will work at the intersection of modeling, data engineering, and operational excellence, with a strong emphasis on:

• Designing and maintaining efficient, reliable data pipelines
• Implementing robust QA and monitoring processes
• Ensuring consistent, explainable, and client-ready outputs

While strong modeling and statistical skills are essential, this role is less R&D-focused and more execution- and delivery-oriented, with ownership over how models and methodologies perform in production over time.

You will collaborate closely with Engineering, Product, and Operations teams to operationalize methodologies, improve data quality, and support ongoing product evolution. This role does not focus on people management, but requires strong judgment, independence, and end-to-end ownership.

• Design, build, and maintain models and analyses that transform alternative data sources (e.g., receipts, clickstream, credit card data) into market share, sales, and competitive insight metrics
• Build and validate media spend estimation methodologies that combine observed signals, coverage assumptions, calibration factors, external benchmarks, and uncertainty ranges
• Develop channel sizing models that estimate market, brand, retailer, category, and media-channel opportunity from incomplete or biased source data
• Translate noisy source signals, such as ad occurrences, receipts, transaction records, clickstream events, panel data, and vendor feeds, into explainable spend, share, and channel-size outputs
• Develop and maintain projection, normalization, and estimation methodologies suitable for production use
• Combine overlapping and complementary data sources into coherent, scalable estimation frameworks
• Develop new analyses and support ongoing model iteration with a strong focus on stability, explainability, and consistency
• Partner with Engineering to design and maintain efficient, scalable data pipelines from raw ingestion through client-facing outputs
• Define and implement data quality checks, validation rules, and monitoring across the pipeline
• Investigate and resolve data anomalies, breaks, or regressions in collaboration with Ops and Engineering
• Own the scientific and methodological integrity of production outputs, including documentation and change management

• Work closely with Operations teams to ensure smooth ongoing production and delivery of insights
• Support Product teams with methodological input for roadmap decisions, client questions, and product enhancements
• Perform ad-hoc and exploratory analyses to support client inquiries, data investigations, and internal decision-making
• Help establish best practices for reproducibility,…