Data & Analytics Manager - Supply Chain

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The Manager of Data & Analytics will be responsible for elevating the analytical capabilities within the Ross Supply Chain and leading sophisticated analysis in support of strategic projects and senior executives. Drives analytic excellence by identifying and adopting new analytical methodologies and increasing business access to data. An integral part of the business, helping to drive decision making, execution and assessment through rigorous analysis.

Expected to focus on project-based (and ad‑hoc) work supporting key strategic priorities for the business. Works in close partnership with the Engineering team and other cross-functional partners. Will keep current with the latest academic research in data science and identify new business opportunities that can be solved using machine learning.

Perform practical, action oriented, & comprehensive data analysis to diagnose business problems, optimize business approaches and measure impact—supporting multiple functions.

• Lead analytics for select cross‑functional initiatives (e.g. region split, new format improvement, roll‑out strategy for new business opportunities). This will require working closely with areas such as Marketing, Planning, Store Operations, DCs and others.
• Play the role of “Subject Matter Expert” for analytics on these projects.
• Flexibly support different types of analysis (e.g. manipulating large data sets, building models, statistical analysis), leveraging understanding of the off‑price business/Ross/dd's to drive insights and recommendations.
• Independently summarize and communicate insights and recommendations with business partners.
• Drive analytic excellence.
• Independently identify opportunities for better data access, build roadmap for advanced analytics at Ross, understand the requirements, and get leadership support.
• Keep abreast of new analytical/statistical methodologies or capabilities, e.g. through annual conversations with vendors, or ad‑hoc investigation when new analysis requires new techniques.
• Recommend upgrades or external trainings as needed.
• Adopt new technologies and/or methodologies and train others on them.
• Lead the build out of advanced data capabilities at Ross, while effectively communicating and partnering with the appropriate parties. Communicate analytics and recommendations in an easy‑to‑understand way (written and verbal).
• Develop compelling materials to communicate results of analysis and key insights.
• Become the point person/subject matter expert for ad‑hoc questions from cross‑functional business partners.

• Problem Solving
• Technical/Functional Skills
• Business Acumen
• Technical Learning
• Intellectual Horsepower
• Peer Relationships/Collaboration
• Learning on the Fly
• Drive for results

• Top‑tier college degree, plus MBA or Master’s degree in a quantitative field.
• Minimum 5 years post‑college experience, with experience in analytical positions. Relevant retail/Supply Chain experience preferred.
• Outstanding analytical ability: data‑driven approach to solving problems with strong quantitative skills and a good sense of how to structure problems.
• Project Management: ability to set priorities between multiple projects and deadlines, and keep senior team updated frequently.
• Detail‑oriented approach: close attention to details and the drive to get things right.
• Strong fundamental business skills (especially analytic): experience in SQL, Power BI and/or Tableau, Excel and PowerPoint, plus at least one large data mining tool or statistical programming language (e.g. SPSS, Python, R).
• Clear understanding of basic financial, statistical and economic concepts.
• Advanced financial and statistical modeling skills and database analysis preferred.
• Teamwork/Communication: ability to build effective working relationships across the organization; ability to listen, understand, and communicate with executives at all levels and translate data analyses into recommendations in a clear, understandable way.
• Experience with statistical analysis, regression modeling, data mining, and machine learning.