Operations Analytics Lead - Returns & Reverse Logistics

RESPONSIBILITIES:

• Build and maintain dashboards and automated reporting for core KPIs — return rate, days-to-refund, refund accuracy, inventory, and defect rates by return center, carrier, and category; ensure data quality and governance across the team
• Measure and optimize warehouse productivity — analyze TPH, units/shift, and team performance; conduct workforce analytics to optimize staffing levels, scheduling, and skill allocation; forecast labor demand and identify inefficiencies
• Diagnose and improve refund accuracy and quality — investigate delayed/incorrect refunds and build preventive monitoring; analyze refurbish metrics (Grade-A rate, bin grades, rework defects); identify process gaps and root causes
• Identify and drive cost reductions — quantify opportunities across labor efficiency, materials, processing errors, shrinkage, and network optimization; prioritize by impact and support ROI modeling for capital and staffing investments
• Build predictive models and anomaly detection — flag processing delays, quality drops, and cost spikes in real time; optimize reverse logistics network performance and refund pipeline bottlenecks
• Partner on system improvements and cross-functional initiatives — collaborate with engineering and operations on data instrumentation; contribute to customer experience and supply chain optimization efforts

QUALIFICATIONS

• Bachelor's degree in a quantitative discipline (e.g., Economics, Mathematics, Statistics, Engineering, Physics, Computer Science, Industrial Engineering), Master’s preferred
• 7+ years of relevant experience in data analytics, operations analytics, or supply chain analytics—preferably with exposure to returns, reverse logistics, or warehouse operations
• Expert-level SQL proficiency; ability to write complex queries, optimize performance, and work with large-scale transactional datasets
• Proficiency in Python or R for statistical analysis, data modeling, and exploratory analysis
• Strong analytical skills: ability to define metrics, diagnose root causes, and translate data insights into actionable business recommendations
• Experience with dashboarding and data visualization tools (Looker, Tableau, Sigma, or similar); familiarity with automation and alerting frameworks
• Strong proficiency with AI tools to increase productivity, surface insights, and accelerate analysis
• Excellent written and oral communication; ability to explain complex analyses to both technical and non-technical audiences
• Proven ability to manage multiple workstreams, prioritize effectively, and deliver in a fast-paced environment
• Strong ownership mentality: self-directed, proactive, and accountable for end-to-end quality and impact

PREFERRED QUALIFICATIONS

• Experience in e-commerce, logistics, or marketplace operations
• Familiarity with reverse logistics, returns management systems, or warehouse workflows
• Background in process optimization, Six Sigma, or operational excellence
• Knowledge of inventory management, cost accounting, or P&L analysis

The ideal candidate is a self-motivated problem-solver who excels at using data and analytics to optimize complex operational processes. They are energized by the opportunity to drive measurable improvements in returns center efficiency, refund accuracy, and product quality. This candidate thrives in a fast-paced, metrics-driven environment where they own end-to-end analytics initiatives—from problem definition and data validation through implementation and impact measurement.

They are comfortable operating at the intersection of operations, finance, and product, translating technical analysis into actionable recommendations for leadership and cross-functional teams. The ideal candidate combines strong analytical rigor with business acumen, understanding how reverse logistics impacts both customer experience and company profitability. They are excited by a culture where transparency, speed, and accountability are core operating principles, and where data-driven insights directly influence strategic and operational decisions.