Über
Make your mark on the legacy of Walmart and come build the next great retail disruptor of global online marketplaces. With Walmart eCommerce, you will redefine the future of shopping and create innovative solutions alongside supportive teammates while influencing millions of shoppers daily. Walmart Marketplace is a curated community of respected, professional sellers who offer only top-quality, authentic products and best-in-class customer service. Marketplace is an integral part of Walmart's growth strategy and fast-paced innovation is embedded into every aspect of what we are building. The Marketplace team is seeking a Manager of Advanced Analytics, Seller Performance who can translate complex data into insights that help leaders and cross-functional partners make informed decisions, measure success, and shape policies that protect marketplace integrity. In this role, you will build a holistic view of seller performance, identify potential risks, and support controls by leveraging analytics tools and platforms such as Python/SQL/Hive/Hadoop/Cloud, and visualization solutions like Tableau, PowerBI, or Looker. Key focus areas include developing seller operational risk strategies and designing & measuring seller insights and communication approaches that enable seller growth and performance improvement while protecting buyers from non-optimal experiences. This is a highly visible position, partnering closely with senior leaders (Directors and Senior Directors) across business and product teams to define and measure Marketplace success. You will make an impact by: Leading delivery of multiple concurrent analytics initiatives in Marketplace Seller Operational Risk, balancing speed, rigor, and stakeholder alignment in a fast-paced environment Defining the end-to-end analytics strategy for seller operational risk measurement, experimentation, forecasting, and performance management across the 3P Seller lifecycle (onboarding, monitoring, enforcement, reinstatement) Owning a scalable KPI and metric framework (North Star + guardrails) to quantify risk outcomes and business tradeoffs (loss prevention/avoidance, GMV protection, seller friction, ops efficiency, false positives/negatives) Building and maintaining robust operational performance reporting (daily/weekly executive dashboards + deep-dive analyses), with automated anomaly detection and alerting for emerging operational risk patterns Developing reusable analytical assets (SQL/Python notebooks, feature definitions, metric layers, and data marts) enabling consistent measurement across teams and experiments Partnering with Data Science to design, validate, and monitor predictive operational risk models and early-warning indicators; support model evaluation with precision/recall, calibration, drift monitoring, bias checks, and threshold optimization Applying causal inference and experimental design to evaluate risk policies and controls (A/B, quasi-experiments like diff-in-diff / matching where randomization isn't feasible), and translate results into product/policy decisions Designing "test-and-learn" frameworks for operational controls, including power calculations, success criteria, instrumentation requirements, and post-test readouts with clear recommendations Collaborating with Product and Engineering to identify gaps in current operational risk tooling, define requirements, and design scalable data products (self-serve analytics, decisioning insights, enforcement explainability) Driving instrumentation and event taxonomy improvements (logging standards, data contracts, lineage, and SLAs) to increase trust in seller risk signals and reduce measurement gaps Establishing and enforcing data quality monitoring (completeness, timeliness, consistency) and governance for seller risk datasets and feature stores; implementing QA checks and automated validation pipelines Performing deep forensic analyses on seller abuse vectors (identity/linkage, returns/refunds abuse, counterfeit signals, policy evasion), combining structured and semi-structured data sources Building segmentation and cohorting frameworks to understand risk by seller type, category, geography, fulfillment model, and maturity stage; identify leading indicators and intervention points Communicating complex analytical findings through crisp narratives, exec-ready artifacts, and tradeoff discussions; influence cross-functional decisions without direct authority Mentoring and developing analysts; setting standards for analytical rigor, code quality, documentation, and reproducibility (version control, peer review, testing) You'll sweep us off our feet if… You bring experience in eCommerce/commerce platforms (ideally Marketplace/3P sellers) with a strong understanding of seller operational performance, trust & safety, fraud/abuse, and risk controls You're a data storyteller who can translate complex analyses into clear narratives, recommendations, and tradeoff discussions for product and business leaders You thrive in ambiguous problem spaces—you can frame problems, form hypotheses, dive deep into data, and independently drive to answers You operate with strong customer and seller centricity, balancing risk mitigation with seller experience and business growth You've partnered across multiple functions (e.g., Product, Ops, Risk, Compliance, Engineering, Data Science) and can influence without authority You have a strong foundation in statistics and applied analytics, with experience working on large-scale datasets and ensuring analytical rigor You're exceptional at data hygiene and accuracy: collecting, validating, organizing, and communicating insights with strong attention to detail You have hands-on expertise supporting experimentation and causal measurement, including sampling strategy, labeling, EDA, bias checks, defining success metrics, and interpreting results You're highly proficient in SQL and advanced analytics using Python (preferred) and/or R, including building reusable analysis frameworks and production-quality notebooks You have experience with Hive/Spark/Databricks-style distributed querying and optimizing queries/pipelines for scale and performance You've built or automated datasets and pipelines in cloud data architectures (e.g., GCP), and understand concepts like orchestration, data quality checks, and monitoring You have strong experience with BI and visualization tools (Tableau, Looker, and/or Shiny) and can build executive-ready dashboards that drive decisions You collaborate effectively with data scientists and data engineers, writing clear requirements, defining data contracts, and aligning on metrics/feature definitions You're curious, proactive, and comfortable operating in unstructured environments, building structure where none exists You have experience analyzing user behavior and product funnels—cohorting, segmentation, retention/engagement, and identifying friction or abuse patterns You enjoy working in a fast-paced agile environment, managing competing priorities while maintaining high standards for quality and impact Qualifications: BA/BS or Master's in Business, Marketing, Mathematics, Statistics, Computer Science, Engineering, or a related field (or equivalent practical experience) 4+ years of experience in advanced analytics / data analysis, including work in risk management, trust & safety, fraud/abuse, compliance, or seller/platform integrity (eCommerce/marketplace experience a plus) Demonstrated ability to own end-to-end analytics projects—problem framing, stakeholder alignment, execution, and delivery of measurable business impact Strong organizational and program execution skills with a high bar for quality, attention to detail, and the ability to manage competing priorities in a fast-paced environment Excellent communication and influence skills: ability to translate complex analyses into clear, actionable recommendations for cross-functional and executive audiences Proven track record of being a self-starter with high ownership, accountability, and comfort operating in ambiguous problem spaces Preferred Qualifications: Master's Degree in Mathematics, Statistics, Data Science or a related technical field Background in Marketplace Analytics, Retail and/or eCommerce Analytics Retail and/or eCommerce industry experience in a heavily data-driven environment Risk management and application of analytics At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. Bentonville, Arkansas US-30008: The annual salary range for this position
Sprachkenntnisse
- English
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