Logistics Optimization Manager

BI, Data Visualization & AI-based Automations

• Design, build, and maintain scalable dashboards and automated reporting pipelines covering fleet performance, delivery speed, order stacking, cost per delivery, and rider utilization
• Own the end-to-end data lifecycle: from raw data extraction (SQL, Big Query) through to business-ready visualizations (Tableau, Looker, Data Studio)
• Automate recurring logistics reports to reduce manual data pulling and free leadership time for decision-making
• Develop predictive models and forecasting tools for demand planning, supply crunch detection, and staffing efficiency
• Translate complex, large-scale datasets into clear, actionable recommendations for logistics and operations stakeholders

• Reduce delivery time across all stages: from order dispatch through to delivery, by identifying bottlenecks across the multiple DT sub-metrics
• Collaborate with dispatching and algorithm teams to enhance order assignment logic and improve on-time delivery rates
• Monitor and improve order stacking efficiency without compromising delivery SLAs
• Track NPS and customer experience metrics tied to logistics performance, identifying drivers of dissatisfaction
• Own analysis of logistics failures across vendor operations, rider operations, and customer touchpoints; recommend and execute fixes
• Collaborate with workforce planning to optimise shift models, staffing levels, and on-demand-to-shift model transitions
• Identify demand patterns and partner with growth and marketing teams to improve forecasting accuracy
• Support rider hiring, onboarding, and payment process analysis to reduce attrition and improve supply reliability

• Act as the logistics liaison between local business stakeholders, local operations teams, and global product teams
• Gather operational pain points, feature requests, and platform feedback; translate these into structured business cases for product prioritisation
• Lead go-to-market (GTM) execution for new logistics initiatives: coordinating timelines, stakeholders, training materials, and deployment playbooks.
• Support rollout and adoption of tools, ensuring operational readiness and post-launch performance tracking
• Track rollout KPIs, adoption rates, and feedback loops, and present findings to product and logistics leadership

• Serve as the first escalation point for logistics issues: diagnosing root causes, mobilizing fixes, and communicating resolution timelines.
• Manage performance during high-demand periods, including holidays, national days, and weekends, with proactive monitoring frameworks.
• Coordinate with central/regional logistics and product teams (e.g. Delivery Hero central) to implement platform-wide improvements locally.
• Minimize wastage per order through operational audits and targeted efficiency initiatives.
• Provide training, change management support, and communications for stakeholder transitions tied to new logistics models or tools

• 5–8 years of experience in logistics analytics, operations, product operations, or a related field, preferably within food delivery, quick commerce, or a high-growth tech platform.
• Proven track record in at least two of: BI/analytics delivery, logistics optimization, and product rollout execution.
• Experience working in multi-market or global organizations with cross-functional, matrixed team structures.
• Exposure to rider/fleet operations, last-mile delivery dynamics, and demand-supply management is strongly preferred
• Exposure to A/B testing frameworks and statistical significance analysis in an operational context.
• Advanced SQL proficiency, can write complex queries from scratch and independently pull your own data.
• Strong experience with data visualization platforms:
  Tableau, Looker, Google Data Studio, or equivalent.
• Comfortable with Python or R for automation, modeling, or data transformation.
• Familiarity with project management and collaboration tools:
  Jira, Confluence, Notion, Asana

• Bachelor's degree in Engineering, Supply Chain, Operations Management, Data Science, Computer Science, or a related quantitative field.
• Master's degree in a relevant field is a plus