Principal, Data Scientist, Experimentation Sciences

About the role

Position Summary...As a Principal Data Scientist at Walmart, you will define and execute the data science roadmap for the experimentation platform that powers trusted decision-making across Walmart’s A/B testing ecosystem. This is a hands-on technical leadership role at the intersection of experimentation science, large-scale data systems, and AI evaluation. You will own the scientific direction behind experiment reporting, dashboards, guardrails, and reusable measurement services, ensuring experiment exposure data is stitched to business and operational outcomes with rigor, scalability, and clarity. You will partner closely with engineering, product, and business teams to modernize our statistical tooling, improve self-service experimentation, and extend our measurement framework to emerging AI use cases including LLM evals, prompt evaluation, hybrid human/LLM judging, and offline-to-online quality measurement. We are looking for a self-starter who can move fluidly from strategy to hands-on prototyping, quickly validating ideas through lightweight automated workflows and proofs of concept.

Responsibilities

• Define the multi-year data science roadmap for experimentation reporting, dashboards, and measurement services, identifying the highest-leverage investments in methodology, automation, and self-service.
• Lead the design of scalable statistical frameworks for online experiments across product, business, and operational use cases, including guardrails, heterogeneity analysis, sequential decisioning, variance reduction, and quasi-experimental methods when randomized tests are not feasible.
• Partner with data engineering to design robust SQL and PySpark data models, pipelines, and observability standards that improve correctness, speed, and reusability of experimentation data assets.
• Establish and govern canonical experiment metrics, scorecards, and reporting standards across channels, regions, and surfaces.
• Define the strategy for AI-native experimentation and evaluation, including LLM eval frameworks, prompt evaluation, golden datasets, rubric design, human-in-the-loop review, LLM-as-a-judge calibration, and ongoing regression monitoring.
• Build lightweight proofs of concept and small automated workflows using tools such as Python, SQL, Airflow, and Google Cloud Platform technologies to validate ideas before broader platform investment.
• Serve as the senior technical advisor to leaders across product, engineering, and business on experimental design, causal interpretation, metric tradeoffs, and measurement risk.

Requirements

• Deep expertise in experimentation, causal inference, and statistical decision-making, with a track record of shaping how organizations design, analyze, and operationalize experiments at scale.
• Expert-level SQL and PySpark, strong Python skills, and hands-on experience working with high-volume, distributed data pipelines in production environments.
• Experience building or materially improving experimentation platforms, measurement systems, or internal science tooling rather than only delivering one-off analyses.
• Strong understanding of metric design, guardrails, data quality, and observability for experimentation systems, including sample ratio mismatch, exposure correctness, and downstream metric integrity.
• Self-starter mindset, with the ability to work through ambiguity, define a roadmap, and independently drive ideas from concept to execution.
• Experience in e-commerce, retail, marketplace, logistics, last-mile delivery, or other high-scale consumer platforms with complex operational feedback loops.
• Working knowledge of modern AI evaluation methods, including LLM evals, prompt experimentation, model or prompt regression testing, and hybrid human-plus-automated quality frameworks.
• Ability to translate ambiguous business problems into rigorous analysis plans, technical designs, and executive-ready recommendations.

Qualifications

• Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, Operations Research, or related field and 10 years’ experience in data science, experimentation, measurement science, or related field.
• Master’s degree in one of the above fields and 8 years’ relevant experience.
• PhD in one of the above fields and 6 years’ relevant experience.

Preferred Qualifications

• Experience building or scaling experimentation platforms, internal measurement tooling, or self-service analytics capabilities.
• Experience supporting high-volume A/B testing in e-commerce, marketplace, or last-mile environments.
• Deep knowledge of advanced experimentation methods such as CUPED/CUPAC, switchback designs, cluster randomization, interference and network effects, Bayesian or sequential testing, and observational causal inference.
• Experience defining AI evaluation frameworks for conversational AI, search, recommendation, or other LLM-powered products.
• Experience with Google Cloud Platform, Airflow, and modern orchestration, monitoring, and data workflow patterns.
• Publishations, patents, or conference contributions in experimentation, causal inference, AI evaluation, or applied machine learning.
• Successful completion of one or more assessments in Python, Spark, Scala, or R.
• Using open source frameworks (for example, scikit learn, tensorflow, torch).

Benefits

Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include : Stock

Compensation

The annual salary range for this position is $143,000.00 - $286,000.00 in Sunnyvale, California US-11657 and $110,000.00 - $220,000.00 in Bentonville, Arkansas US-10735. Additional compensation for certain positions may also include : Stock