Manager, Advanced Analytics - Seller Performance

Walmart Walmart · Retail · Bentonville, AR +1

Manager of Advanced Analytics, Seller Performance at Walmart, focused on building a holistic view of seller performance, identifying risks, and supporting controls using analytics tools. The role involves defining analytics strategy, owning KPI frameworks, building operational reporting, developing analytical assets, partnering with Data Science on predictive models, applying causal inference for policy evaluation, designing test-and-learn frameworks, and collaborating with Product/Engineering on data products. It also includes driving instrumentation, data quality monitoring, performing forensic analyses on abuse vectors, and building segmentation frameworks. The role requires experience in eCommerce, data storytelling, statistical foundations, and supporting experimentation.

What you'd actually do

  1. Lead delivery of multiple concurrent analytics initiatives in Marketplace Seller Operational Risk, balancing speed, rigor, and stakeholder alignment in a fast-paced environment
  2. Define 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)
  3. Own 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)
  4. Build and maintain robust operational performance reporting (daily/weekly executive dashboards + deep-dive analyses), with automated anomaly detection and alerting for emerging operational risk patterns
  5. Develop reusable analytical assets (SQL/Python notebooks, feature definitions, metric layers, and data marts) enabling consistent measurement across teams and experiments

Skills

Required

  • Python
  • SQL
  • Tableau
  • PowerBI
  • Looker
  • statistics
  • applied analytics
  • large-scale datasets
  • analytical rigor
  • data hygiene
  • data accuracy
  • experimentation
  • causal inference
  • experimental design
  • eCommerce/commerce platforms
  • seller operational performance
  • trust & safety
  • fraud/abuse
  • risk controls

Nice to have

  • Hive
  • Hadoop
  • Cloud
  • Marketplace/3P sellers
  • data storytelling
  • ambiguous problem spaces
  • customer and seller centricity
  • influence without authority
  • test-and-learn frameworks

What the JD emphasized

  • seller operational risk
  • risk controls
  • predictive operational risk models
  • evaluate risk policies and controls
  • operational risk tooling
  • seller risk signals
  • seller abuse vectors
  • operational risk

Other signals

  • Develop reusable analytical assets
  • Partner with Data Science to design, validate, and monitor predictive operational risk models
  • Apply causal inference and experimental design to evaluate risk policies and controls
  • Collaborate with Product and Engineering to identify gaps in current operational risk tooling, define requirements, and design scalable data products