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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in data science and applied machine learning, with a strong focus on architecting large-scale ML systems and driving MLOps practices. Proven ability to influence stakeholders and mentor senior engineers while contributing to thought leadership in the field.
Highest-signal resume keywords
Data Science ExpertiseMachine Learning ArchitectureStatistical ModelingMLOps ImplementationStakeholder Influence
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical ModelingMachine LearningCausal InferenceProduction-Quality PythonProduction-Quality ScalaProduction-Quality SQLDistributed Data SystemsFeature StoresStreaming PipelinesCloud Platforms
Soft Skills
MentoringInfluencing StakeholdersCross-Organizational Collaboration
Tools & Technologies
SparkGCPAzureAWSCI/CD
Industry Keywords
Retail DataSupply Chain DataOmnichannel CommerceEnterprise Data ProductsInternational Markets
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonScalaSparkSQL
About the role
Key responsibilities & impact- Set the technical vision and multi-year roadmap for data science and applied ML across multiple product lines
- Architect and guide large-scale ML/statistical systems for forecasting, causal inference, anomaly detection, optimization, personalization, and GenAI/LLM applications
- Serve as the top technical escalation point for complex, cross-organizational data science problems
- Partner with VPs, Senior Directors, and Product leadership on technical roadmaps and build-vs-buy, platform, and architecture decisions
- Mentor Principal and Staff Data Scientists/Engineers and conduct rigorous design and code reviews
- Drive adoption of MLOps, experimentation, and responsible-AI practices
- Identify and de-risk technical blockers and surface risks, trade-offs, and mitigation paths to leadership
- Represent Walmart's data science capability through publications, patents, conference talks, and technical communities
- Partner with Data Engineering, Platform, and Infrastructure teams on scalable data foundations
- Prototype, write and review production-grade code/models, and make final technical decisions on difficult problems
Requirements
What you’ll need- 12+ years of professional experience in data science, applied machine learning, or statistics, including demonstrated enterprise-scale production impact
- Master's or PhD in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field (or equivalent practical experience)
- Deep, hands-on expertise in statistical modeling, machine learning, and/or causal inference
- Experience architecting and operating large-scale, distributed data/ML systems, including Spark, distributed training, feature stores, and streaming pipelines
- Production-quality Python/Scala/SQL, testing, CI/CD, and cloud platforms (GCP, Azure, or AWS)
- Ability to influence and align VP/SVP-level stakeholders and engineering organizations without direct authority
- Experience mentoring Staff/Principal-level engineers and data scientists
- Experience in retail, supply chain, or omnichannel commerce data domains
- Experience applying LLMs/GenAI to enterprise data products, including RAG, agents, evaluation frameworks, and guardrails
- Published research, granted patents, or recognized external thought leadership in ML/AI
- Experience operating across international markets with varying data maturity and regulatory constraints
- Prior experience as a technical founder, chief architect, or Distinguished/Fellow-level engineer at another large-scale technology company
- Minimum qualifications: Bachelor's degree plus 6 years' analytics experience, Master's degree plus 4 years' analytics experience, or 8 years' experience in an analytics or related field
Benefits
Comp & perks- Incentive awards for performance
- Maternity and parental leave
- PTO
- Health benefits
- Compensation package