Revenue Management Specialist - Pricing Analytics & Automation

Role purpose

Pricing analytics and revenue management

• Analyze customer, shipment, revenue, price, margin, and operational data to identify price dispersion, profitability gaps, leakage, and repricing opportunities.
• Build recurring views of yield, price realization, customer and lane economics, surcharge performance, and commercial trends.
• Build annual price review and GRI analytics, customer-level targets, and controls tracking implementation and realized like-for-like uplift.
• Segment customers and pricing archetypes; compare actual shipment behaviour with contracted profiles and quantify commercial impacts of deviations.

Tools, automation, and decision support

• Build robust Excel MVPs for GRI, profitability, surcharge leakage, tender support, price corridors, segmentation, exception monitoring, and account review.
• Design models with clear inputs, controls, documentation, and refresh logic; standardize source files, calculations, and output formats.
• Use Microsoft 365 to automate data ingestion, cleaning, transformation, reconciliation, and recurring reporting where practical.
• Establish data-quality checks, version control, and auditability; document data requirements and analytical logic for potential enterprise systems.
• Produce dashboards and management views, support pricing and tender cases, and communicate findings, financial impact, and recommendations.

Education and experience

• Bachelor’s degree in Finance, Economics, Business Analytics, Engineering, Mathematics, Statistics, Supply Chain, or a related quantitative discipline.
• Typically 3–6 years in pricing analytics, revenue management, commercial analytics, business intelligence, commercial finance, or a similar analytical role.
• Experience analysing large transactional datasets and turning them into commercial recommendations is essential.
• Logistics or transportation experience is advantageous but not mandatory.

Technical and communication skills

• Fluent business English and clear presentation of analytical conclusions to non-technical stakeholders are essential.
• Advanced-to-expert Excel is critical, including PivotTables, XLOOKUP or INDEX-MATCH, SUMIFS/COUNTIFS, dynamic arrays, structured tables, data validation, error controls, and scenario modelling.
• Strong Power Query capability and working knowledge of Excel Data Model / Power Pivot and relational data concepts are required.
• Basic DAX is highly desirable. Power Automate and/or Office Scripts are desirable; VBA is useful but not primary.
• SQL is a strong advantage. Python or R is nice to have, not a requirement.

Leadership behaviors