Senior Specialist, Commercial Analytics
SwedenCompetitive0 applicants
About this role
We are seeking a highly analytical and strategic individual to join our regional logistics team as a (Senior) Specialist, Logistics Strategy & Performance.
In this high-impact role, you will drive logistics operations and supply economics across 16 diverse foodora & foodpanda markets. Acting as the bridge between business strategy and on-the-ground execution, you will manage critical supply levers that impact tens of thousands of riders and millions of customers. Your core focus will center on elevating our supply operations, from driving daily operational excellence through precise forecasting, to optimizing unit economics and engagement frameworks for the fleet to balance financial efficiency and service levels.
Responsibilities
- Forecasting & Daily Staffing: Take ownership of the daily demand planning and fleet staffing process. Analyze demand patterns, refine forecasting models, and execute shift planning to ensure optimal fleet coverage, balancing service level targets with cost efficiency.
- Supply Economics & Engagement Strategy: Design, build, and optimize marketplace supply levers and fleet engagement models. You will develop frameworks that balance recruitment with earnings – such as predictive pay and incentives – to drive courier engagement, allowing for necessary local adaptations, while maintaining consistency and financial discipline.
- Financial Impact & Cost Efficiency: Continuously evaluate the ROI of fleet investments and operational structures, rigorously monitoring cost-per-order (CPO) trends. Ensure all supply-side strategies align with broader profitability targets while maintaining a healthy, engaged fleet.
- Market Performance Ownership: Take full ownership of logistics performance for key European markets. Proactively monitor daily trends, diagnose operational bottlenecks, and implement data-backed solutions to improve delivery times and overall fleet efficiency.
- Data Analysis & Automation: Dive deep into large, complex datasets to identify t