Reverse Logistics Data Analyst

Preferred Qualifications

• Bachelor's degree in Supply Chain, Business Analytics, Finance, Statistics, or related field.

• 2+ years of experience in data analytics, supply chain analytics, or reverse logistics.

• Advanced proficiency in Excel (pivot tables, Power Query, complex formulas).

• Strong analytical, problem-solving, and root cause analysis skills.

• Ability to translate complex datasets into actionable insights.

• Experience in telecommunications or consumer electronics supply chain.

• Knowledge of device lifecycle management and refurbishment operations.

Core Competencies

• Data-driven decision making

• Operational risk assessment

• Process improvement mindset

• Stakeholder communication

• Attention to detail

Requirements:

Position Summary

The Reverse Logistics Data Analyst is responsible for analyzing, optimizing, and reporting on device return, recovery, refurbishment, and disposition processes within AT&T's supply chain network. This role leverages data analytics to improve asset recovery rates, reduce losses, streamline reverse supply chain operations, and ensure compliance with operational and financial controls.

The ideal candidate combines strong analytical expertise with operational acumen in telecommunications device lifecycle management.

Key Responsibilities

Data Analysis, Research & Reporting

• Analyze reverse logistics data including device returns, RMA trends and trade-ins

• Identify root causes of processing delays, loss exposure, or compliance gaps.

• Perform variance analysis across all systems.

Process Optimization

• Partner with Supply Chain, Retail Operations, Finance, and IT to streamline reverse logistics workflows.

• Identify process bottlenecks and recommend automation or system enhancements.

• Analyze carrier-related issues (e.g., shipment discrepancies, scan compliance, damage trends).

Financial & Inventory Controls

• Monitor financial impact of returns, write-offs, credits, and vendor chargebacks.

• Support reconciliation efforts between physical inventory and system records.

• Assist in forecasting reverse volume trends and recovery projections.

Cross-Functional Collaboration

• Work with distribution centers, field leadership, AR/retail teams, and vendors to resolve data discrepancies.

• Provide executive-level reporting and insights.

• Support strategic initiatives related to device lifecycle management and sustainability goals.