(USA) Senior, Data Analyst

Position Summary...

As a Senior Data Analyst at Sam’s Club, you will play a vital role in supporting the fuel business with advanced analytics and data-driven decision-making. You’ll partner with cross-functional stakeholders to translate business challenges into actionable insights, enabling smarter, faster, and more strategic operations. In this highly visible role, you will work with large-scale datasets and evolving platforms to uncover patterns, automate reporting, and build predictive models. This is an exceptional opportunity for someone who thrives at the intersection of business context, statistical depth, and innovative technologies like machine learning and generative AI.

What you'll do...

In your first 12–18 months, you’ll take ownership of these critical performance objectives: 1. Lead End-to-End Fuel Analytics Projects Across Regions
Within 60–90 days, take ownership of the analytics lifecycle for major regional or national Fuel operations requests. Collaborate with fuel leaders, understand KPIs, extract and clean data, and deliver clear, high-impact insights through dashboards or data stories. Results will influence business strategy, resource allocation, and frontline execution. 2. Develop Scalable Reporting Tools and Decision Support Systems
By the end of your first 6 months, create and operationalize automated dashboards and reporting solutions using tools like PowerBI or Tableau, ensuring they are fully adopted by business users and drive measurable productivity improvements. 3. Build Predictive and Prescriptive Statistical Models for Key Fuel Use Cases
By month 9, deliver statistical and machine learning models (e.g., regression, clustering, time series forecasting) that address high-priority business scenarios such as demand planning, labor optimization, or member churn. Clearly communicate model performance, business implications, and recommended actions. 4. Champion Data Strategy and Governance Practices
Continuously improve data accuracy, usability, and trust. Within your first 120 days, identify 3+ recurring data quality issues, propose solutions, and partner with data engineering or governance teams to ensure long-term resolution and traceability. 5. Translate Business Problems into Analytical Approaches Using GenAI and Emerging Methods
By month 6, design and test at least two experimental approaches using generative AI or large language models (e.g., for text classification or scenario generation) in fuel analytics workflows. Collaborate with engineering and business teams to assess feasibility and value. What You’ll Bring You are a curious, business-oriented analyst who thrives in ambiguity and can connect dots across data, people, and strategy. You bring 4–6 years of experience in an applied analytics role with demonstrable achievements in solving complex business problems using data. You’ve worked with distributed data sources (SQL, NoSQL, cloud environments), and are comfortable in both exploratory and production-grade analysis. You have hands-on experience with tools such as Python, R, or Scala, and can explain technical outcomes to non-technical stakeholders with clarity and conviction. Familiarity with machine learning models and a growing interest in generative AI put you at the frontier of modern analytics. You’re passionate about impact — not just dashboards. Critical Subtasks These foundational tasks enable you to deliver the outcomes above. You’ll: 1) Partner with stakeholders across regions to frame ambiguous business problems into clearly defined analytical questions that drive measurable business outcomes. 2) Conduct structured exploratory data analysis using advanced statistical techniques and tools (e.g., Python, R, Excel) to uncover actionable trends, outliers, and root causes. 3) Design, build, and maintain robust data pipelines using Python and SQL to extract, transform, and load data from multiple distributed sources, ensuring accuracy, performance, and reusability. This is a must-have skill. 4) Develop and maintain dynamic dashboards using PowerBI to enable real-time visibility into key operations metrics, ensuring adoption and usability across business audiences. 5) Proactively monitor, validate, and troubleshoot datasets and queries used in key dashboards and reporting tools. Prioritize solutions that ensure data reliability and speed. 6) Collaborate cross-functionally with data engineering and IT teams to resolve upstream data quality issue