(USA) Principal, Data Scientist
About The Position
Walmart is seeking a Principal Data Scientist for the Media Science and Experimentation team supporting Sam’s Club Connect (SCC), the retail media network of Sam’s Club. The Measurement, Insights and Data Strategy (MINDS) team provides campaign performance measurement and audience insights, and leads media experimentation and data strategy to improve marketing effectiveness and support growth for supplier brands and Sam’s Club. This is a hands-on technical leadership role focused on defining, building, and scaling reusable data science capabilities for incrementality measurement, media optimization, and emerging advertising experiences. The role develops and productizes media mix models for budget planning, scenario simulation, and in-season optimization; builds scalable Search Ghost Ads incrementality measurement; and leads experimentation and measurement for Sponsored Product Ads and new ad formats. It also applies machine learning, causal inference, and AI-driven capabilities to improve measurement and decision-making at scale. The Principal Data Scientist sets scientific direction and technical standards, leads complex cross-functional initiatives from problem definition through production and adoption, and influences Product, Engineering, Advertising, and business leaders. The role mentors data scientists and translates complex analyses into actionable recommendations for supplier investment, product strategy, campaign performance, and SCC revenue growth.
Requirements
- Master’s degree or higher in Computer Science, Machine Learning, Mathematics, Statistics, Operations Research, or a related quantitative field, with 10+ years of industry experience applying machine learning, statistics, and analytics to complex business problems, as stated in the role qualifications.
- Experience with hypothesis testing, p-values, confidence intervals, and regression analysis.
- Ability to set technical direction, lead complex cross-functional data science initiatives, influence senior stakeholders, and drive solutions from scientific development through production and business adoption.
- Hands-on experience designing, implementing, and scaling production-grade modeling systems, including model training pipelines, offline and online evaluation, monitoring, and retraining.
- Hands-on experience building Media Mix Models and applying them to budget planning, optimization, and scenario analysis, including saturation curves, diminishing returns, marginal ROAS, priors, and Bayesian or frequentist approaches.
- Experience with causal inference and quasi-experimental methods for incrementality measurement, including A/B testing, power analysis, difference-in-differences, matched controls, synthetic controls, and Bayesian structural time series.
- At least 2 years of hands-on experimentation and causal machine learning experience informing advertising and product decisions.
- Background in machine learning and statistical modeling, including advertiser segmentation and ad response prediction, such as logistic regression, gradient-boosted trees, uplift modeling, and bias–variance tradeoffs; exposure to keyword or bidding optimization problems.
- High proficiency in Python and SQL.
- Ability to navigate ambiguity, communicate scientific tradeoffs to technical and nontechnical audiences, mentor data scientists, and build alignment across teams.
- Minimum qualifications listed: bachelor’s degree in a specified quantitative or related field plus 5 years of analytics-related experience; or master’s degree in a specified quantitative or related field plus 3 years of analytics-related experience; or 7 years of experience in an analytics or related field.
Nice To Haves
- Experience with large-scale data environments such as Google BigQuery and Dataproc, including distributed data processing and feature pipeline development.
- Experience with TensorFlow or similar machine learning frameworks.
- Tableau experience for visualizing advertising performance.
- Familiarity with Google Meridian or similar media mix modeling platforms.
- Familiarity with GenAI or LLM-based tools for automation, scalable analytics, and insight generation.
- AI/ML Engineering experience building scalable media measurement and targeting solutions.
- Prior Sponsored Product Ads or retail media domain experience.
- Preferred qualifications include data science, machine learning, optimization models, a PhD in a relevant field, publications or active peer review in related journals or conferences, and successful completion of one or more assessments in Python, Spark, Scala, or R.
- Experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
- Knowledge of inclusive digital experiences, WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices.
Responsibilities
- Set technical direction for scalable measurement and optimization capabilities, define the scientific roadmap and methodological standards, and lead initiatives through production, adoption, and business impact.
- Build and validate Search Ghost Ads incrementality capabilities and causal approaches for campaign-level measurement, supplier investment, campaign optimization, and ad-serving decisions.
- Develop and productize OMNI-Impact Pulse and media mix model solutions for in-season optimization, budget planning, scenario analysis, and media mix recommendations; partner with Product and Engineering on Ad Center integration and supplier-facing recommendations.
- Design, build, and productize granular media mix models across sponsored media and advertiser segments to support budget allocation, optimization, and scenario analysis.
- Lead experimentation and causal measurement for Sponsored Product Ads and new ad launches, using A/B testing where possible and quasi-experimental or causal inference methods when randomized experiments are not feasible.
- Develop measurement approaches for emerging ad experiences, including Expert Videos and Seasonal Pages, and translate results into product and investment recommendations.
- Build repeatable incrementality measurement frameworks that quantify media investment impact and improve on simple pre/post analyses.
- Develop machine learning and advanced analytics solutions, including advertiser segmentation models and member-level ad response prediction models that support supplier growth and ad performance while protecting the member experience.
- Partner with Product, Engineering, Advertising, and senior business leaders to influence strategy, communicate scientific tradeoffs, align stakeholders, and translate findings into actionable recommendations.
- Mentor data scientists and elevate technical rigor through reviews, knowledge sharing, and guidance on experimentation, causal inference, modeling, and production practices.
- Apply AI-driven capabilities to automate analysis, accelerate insight generation, and scale measurement and optimization workflows.
Benefits
- Performance-based incentive or bonus awards.
- 401(k) with company match.
- Stock purchase plan.
- Medical, vision, and dental coverage.
- Paid maternity and parental leave.
- Paid time off (PTO and/or PPTO), which may be used for vacation, sick leave, holidays, or other purposes.
- Family care leave.
- Bereavement leave.
- Jury duty and voting leave.
- Company-paid life insurance.
- Short-term and long-term disability.
- Company discounts.
- Military Leave Pay.
- Adoption and surrogacy expense reimbursement.
- Live Better U company-paid education benefit, including tuition, books, and fees for eligible programs.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
