Fulfillment by Amazon (FBA) enables sellers to scale their businesses globally by leveraging Amazon’s world-class fulfillment network. Sellers using FBA benefit from fast, reliable shipping, Prime delivery eligibility, and hassle-free returns—allowing them to focus on growth while we handle operations. The WW FBA Central Analytics team builds and operates scalable, enterprise-grade data infrastructure, tools, and analytics solutions that power WW FBA business. We partner across global product, program, and operations teams to unify diverse datasets, deliver self-service analytics, and develop next-generation capabilities using LLMs to unlock insights.

We are seeking a Business Analyst who discovers and communicates the most important opportunities, risks, and root causes across FBA. Beyond building dashboards, you’ll design and run analyses that surface actionable insights (leading indicators, root-cause narratives, recommended actions), automate recurring insight pipelines, and partner with engineering and ML to ensure insights are reproducible and auditable.

Key job responsibilities
- Proactive insight discovery: Regularly mine operational data to surface high-value insights and package them into concise narratives with recommended actions.
- Hypothesis-driven analysis: Translate business questions into testable hypotheses, design experiments or cohort analyses, validate causes, and quantify business impact (uplift/cost savings).
- Automated insight pipelines: Build reproducible analytics workflows that generate recurring insights and integrate them into dashboards, alerting channels, or downstream model training sets.
- Insight operationalization: Work with data engineers and BIEs to productionize key insights—ensure they are versioned, scheduled, monitored, and instrumented with SLAs and lineage.
- Narrative & visualization: Create executive-ready narratives and visualizations that explain root cause, confidence, and recommended next steps; tailor communications for ops, product, and leadership.
- Metrics stewardship: Maintain canonical KPI definitions, derive leading indicators, and ensure derived insights are grounded in trusted metrics.
- GenAI feedback loop: Partner with model and product teams to (a) provide labeled examples and failure cases for the GenAI assistant, and (b) validate AI-surfaced insights against canonical analysis.