Sr. Data Scientist- Digital Decision Science

Domino's Corporate•Ann Arbor, MI

17h

About The Position

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 Senior Data Scientist – Loyalty & Ecommerce Analytics plays a critical role in shaping how Domino’s evaluates and evolves its loyalty and digital experience strategy. This role operates at the intersection of loyalty mechanics, promotions, and ecommerce behavior, delivering insights that inform campaign design, product decisions, and long-term customer value. This role works independently on complex, cross-functional problems, applying deep analytical judgment to ambiguous questions and imperfect data. Success in this role requires strong technical depth, business fluency across loyalty and ecommerce, and the ability to translate analysis into clear, credible narratives that influence decision making and build alignment across teams.

Requirements

Bachelor’s Degree in Data Science, Statistics, Mathematics, Marketing Science, or related field
3+ years of professional related data science experience
Advanced knowledge of a web-analytics tools like Adobe Analytics or similar products
Proficient in SQL querying, with ability to clean, transform, and process data
Proficient in R or Python, with emphasis on statistical analysis.
Applied experience with statistical testing, with firm understanding of test design
Proficient in Microsoft Office – Fluent in Power Point, Excel, and PowerBI or similar products
Excellent project management and collaboration skills

Responsibilities

Own and set direction for complex analyses spanning loyalty performance, perks, lifecycle behavior, eComm AB tests, and ecommerce funnels
Develop and interpret perk projections, Active Member reporting, and post‑campaign analyses to inform campaign selection and prioritization
Apply advanced analytical techniques and sound judgment when clean experimental controls are not available
Frame ambiguous business questions into structured analytical approaches that span loyalty and ecommerce considerations
Connect results to business context, clearly articulating assumptions, tradeoffs, and implications for decision‑making
Deliver clear memos and presentations that synthesize insights for Marketing, Loyalty, Product, and Technology partners
Act as a resource and thought partner to peers and stakeholders across Loyalty, Digital Experience, and Product teams
Explain complex or sensitive findings and work to build alignment and consensus across teams
Establish strong working relationships with subject matter experts across functions to drive shared outcomes
Help advance the team’s analytics practice by adopting AI-enabled tools, automation, and modern workflows where they meaningfully improve insight generation and scale
Contribute reusable frameworks, templates, and analytical patterns that raise team standards and efficiency
Owns and sets direction for complex analytical problems end to end, independently defining approach, success criteria, and decision framing while proactively engaging peers and leadership as needed
Exercises seasoned judgment across multiple sources of information, deliberately selecting methods, tradeoffs, and level of rigor based on business risk, scale, and downstream implications
Raises the bar across the team by modeling strong analytical thinking, elevating storytelling standards, and coaching others to challenge assumptions and strengthen conclusions
Extends impact beyond individual work by establishing reusable frameworks, shared context, and analytical patterns that improve consistency, efficiency, and quality across the team
Builds deep trust with partners by delivering insights that are reliable, well-reasoned, and clearly connected to business realities, tradeoffs, and decision paths