Data Scientist-Digital Decision Science

Company Description

Domino’s Pizza, which began in 1960 as a single store location in Ypsilanti, MI, has had a lot to celebrate lately: we’re a reshaped, reenergized brand of honesty, transparency and accountability – not to mention, great food! In the rise to becoming a true technology leader, the brand is now consistently one of the top five companies in online transactions and 65% of our sales in the U.S. are taken through digital channels.

The brand continues to ‘deliver the dream’ to local business owners, 90% of which started as delivery drivers and pizza makers in our stores. That’s just the tip of the iceberg…or as we might say, one “slice” of the pie! If this sounds like a brand you’d like to be a part of, consider joining our team!

The Decision Science team drives Domino’s industry‑leading growth by pairing strong business thinking with advanced analytics across data science, experimentation, and applied measurement. The team partners closely with Marketing, Loyalty, Product, and Technology to turn data into clear direction that improves customer engagement, loyalty health, and long‑term retention.

The Data Scientist – Digital Loyalty & Ecommerce Analytics role focuses on delivering insights that inform decisions across Domino’s loyalty and promotional ecosystem, including loyalty member perks, active member performance, and campaign effectiveness. This role frames business questions, evaluates performance against established loyalty KPIs, and translates analytical findings into clear implications for Marketing and Loyalty partners. Success in this role requires comfort working with large, complex datasets, strong command of test design and experimental methods, and the ability to communicate results through concise, credible data narratives that influence senior leaders and build trust in the work.

• Own end-to-end analyses related to our key performance indicators and objectives and key results.
• Build and maintain analytical datasets, dashboards, and notebooks to support ongoing monitoring and ad‑hoc analysis.
• Apply appropriate analytical techniques (e.g., pre/post analysis, incrementality, propensity matching) to answer business questions where perfect controls are not available.
• Validate outputs for accuracy, reasonableness, and business feasibility. Exercise judgment across multiple factors and imperfect data, correcting issues and aligning on validation standards before sharing broadly.
• Impact the quality of team deliverables by sharing code, documentation, and context. Provide informal guidance to peers and contribute to improved ways of working, automation, and efficiency.

• Translate ambiguous or loosely defined business questions into structured analytical approaches.
• Connect results to business context, clearly articulating assumptions, limitations, and whether findings are directional or causal.

• Produce memos and presentation decks that summarize findings and implications for Marketing and Loyalty partners related to loyalty enrollment, redemption behavior, campaign rollover, and post‑promotion customer behavior.
• Develop and interpret perk projections, including expected lift, redemption, and financial impact, using historical learnings and agreed assumptions.
• Produce recurring analyses such as Active Member reporting, diagnosing drivers of change and surfacing implications for upcoming campaigns.
• Work within the team’s KPI framework, ensuring analyses tie directly to defined success metrics and decision thresholds.
• Pushes forward by exploring and applying emerging AI‑enabled analytics and tooling, improving how insights are generated, scaled, and embedded into loyalty decision making.

• Owns a defined set of analytical problems end‑to‑end, knowing when to move independently and when to pull in peers or leadership.
• Makes practical judgment calls around approach, tradeoffs, and level of rigor based on the business decision at hand.
• Raises the bar on individual output through strong analytical thinking, clear storytelling, and…