Data Scientist - D&BI, Sam's Club

Overview

At Sam’s Club, we are member obsessed, people led and tech powered. As a Staff Data Scientist – Data & Business Insight at Sam’s Club, you will play a critical role in shaping how data, AI, and advanced analytics improve the in-club Member and Associate experience. You’ll take on high-impact business problems—often ambiguous and cross-functional—and turn them into scalable, data-driven solutions that influence strategic decisions across the organization.

In this role, you’ll operate as both a hands-on technical leader and a trusted thought partner to business and product teams. Success means delivering measurable improvements through experimentation, agentic AI solutions, and predictive models, while elevating the data science craft through mentorship and collaboration.

• Lead high-impact analytical initiatives by framing complex business questions into testable hypotheses, analytical approaches, and deployable AI-driven solutions that support Member, Associate, and Connected Club experiences.
• Design, execute, and scale experimentation (A/B tests, causal inference, and machine learning models) that directly inform product, operational, and strategic decisions.
• Develop and deploy Agentic AI solutions that expand Sam’s Club’s AI capabilities, moving concepts from prototype to production in partnership with engineering and platform teams.
• Transform raw, multi-domain data into insights by sourcing, validating, and modeling large datasets to ensure accuracy, relevance, and business fitness.
• Influence decisions through storytelling by translating analytical findings into clear, compelling narratives and visualizations tailored to senior leaders and cross-functional stakeholders.
• Raise the technical bar for the team by mentoring junior data scientists and analysts in modeling techniques, coding standards, and analytical best practices.
• Collaborate across functions (Product, Engineering, Operations, and Business partners) to ensure analytical solutions are scalable, adopted, and aligned with enterprise objectives.

• A proven track record (4–8 years) of delivering data science or advanced analytics solutions that influenced real business outcomes, not just models or dashboards.
• Strong technical depth in SQL and Python, with hands-on experience using libraries such as pandas, scikit-learn, and stats models to analyze large, complex datasets.
• Practical experience with experimentation including A/B testing, causal inference, and statistical analysis in real-world business environments.
• Working knowledge of Generative and Agentic AI, including building autonomous or multi-agent workflows and integrating them into broader analytical solutions.
• Experience operating in cloud data ecosystems (e.g., Big Query, Snowflake, Spark) and partnering with engineering teams to product ionize models.
• Business and strategic acumen, with the ability to structure ambiguous problems, prioritize impact, and recommend data-driven actions.
• Strong communication and influence skills, able to earn trust, align stakeholders, and drive decisions without formal authority.

The annual salary range for this position is $ - $.

Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include Regional Pay Zone (RPZ) (based on location).

• Option 1:
  Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field.
• Option 2:
  Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field.
• Option 3: 6 years' experience in an analytics or related field.

• Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics.
• Successful completion of one or more assessments in Python, Spark, Scala, or R.
• Using open source frameworks (for example, scikit learn,…