Sr. Transportation Business Analyst

ReturnPro•Miami, FL

About The Position

The Senior Transportation Business Analyst serves as the analytical backbone of Transportation and Supply Chain, translating complex freight, operational, and commercial data into clear insights, root-cause diagnoses, and actionable recommendations. This role is responsible for identifying cost leakage, performance gaps, and optimization opportunities across parcel, freight, inbound logistics, and network strategy, connecting data across systems to support smarter, faster decision-making. This is not a reporting role; it is a decision-support and problem-solving role. This role ensures Transportation and Supply Chain decisions are driven by facts, not assumptions. By connecting freight, operational, and commercial data, the Sr. Transportation Business Analyst helps protect margin, improve service, and enable scalable growth. Key Responsibilities This job description in no way states or implies that these are the only duties to be performed by the teammate occupying this position. The selected candidate may perform other related duties assigned to meet the ongoing needs of the business.

Requirements

5+ years of experience in transportation analytics, supply chain analytics, or business analytics in a logistics-heavy environment
Strong understanding of parcel, LTL, FTL, and inbound logistics cost structures
Proven ability to perform root-cause analysis and translate data into actionable insights
Advanced Excel / spreadsheet skills and experience with BI tools (e.g., Power BI, Tableau, Looker, or similar)
Experience working with TMS, invoice audit data, and operational systems
Strong business judgment and ability to challenge assumptions respectfully and constructively
Analytical and systems thinker
Curious, proactive problem-solver
Comfortable operating with ambiguity
Able to influence without authority
Clear communicator who can translate complexity into clarity
Trusted partner with high integrity and accountability

Responsibilities

Analyze parcel, LTL, FTL, and intermodal transportation data to identify cost drivers, trends, anomalies, and performance issues
Proactively surface issues related to cost per unit, DIM impacts, accessorial charges, claims, service failures, and lane degradation
Monitor transportation KPIs and explain variances with clear root-cause analysis
Connect and reconcile data across multiple systems including TMS, parcel invoice data, internal platforms (e.g., R1), WMS, finance systems, and operational data sources
Bridge freight data with operational performance, facility behavior, and commercial decisions
Partner with Operations, Finance, Sales, and Technology teams to ensure consistent and reliable data interpretation
Translate “what happened” into “why it happened” and “what we should do next”
Identify structural vs. tactical issues (e.g., packaging design, SOP changes, lane strategy, volume shifts)
Quantify financial impact, operational risk, and potential upside of proposed actions
Design and maintain executive-ready dashboards and scorecards for transportation and supply chain leadership
Automate recurring reporting while continuously refining KPIs to ensure relevance and clarity
Eliminate low-value or redundant reporting in favor of insight-driven views
Leverage AI and advanced analytics to identify patterns, anomalies, and exceptions in transportation and operational data
Develop AI-supported narrative summaries that explain performance changes, risks, and opportunities
Support scenario modeling and “what-if” analysis to guide strategic decisions
Support freight and parcel quoting by analyzing historical lane data, cost structures, and assumptions
Validate expected costs and margins for new business or changes in volume
Provide data-driven input to transportation leadership without owning carrier negotiations or commercial commitments
Serve as a trusted analytical partner to Transportation leadership (Parcel, Freight, Network Strategy)
Collaborate closely with Procurement, Finance, Operations, and Sales to align data, assumptions, and outcomes
Present findings and recommendations clearly to senior leadership and cross-functional teams