Data Analyst I - Courier Real-Time Operations

Delivery Hero — Germany · Posted ~2 hours ago

Junior Visa History ✓

Skills

Data analysis Operational analytics Root cause analysis Automation Real-time monitoring AI agent analysis Problem solving Real-time analytics AI agents Voice AI Chat AI

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Summary ✨ AI‑Generated

Join an operations analytics team focused on solving difficult real-time problems. You will investigate root causes, own analyses from start to finish, and build the automations that result from those analyses, including real-time detection, triggered actions, monitoring, and documentation. The role also involves evaluating systems supporting AI voice and chat agents across multiple markets.

Highlights

Own operational analysis end to end and turn ambiguous problems into decisions and automated solutions. The role sits at the intersection of operations analytics, AI-agent quality, and automation engineering, with exposure to real-time systems across multiple markets.

Description

Job DescriptionYOUR MISSION Real-Time Operations keeps Glovo’s deliveries moving: when an order stalls, a courier stops, or a store goes quiet, RTO is what notices and acts. Your mission is to find what is actually causing those failures and then build the thing that fixes them — the analysis and the automation, not one handed to someone else to build. You will work at the intersection of operations analytics, AI agent quality and automation engineering, on problems that are genuinely unsolved. THE JOURNEY Own operational analyses end to end — take an ambiguous problem, find the real driver rather than the most visible symptom, and turn it into a decision about where to focus. Build and run the automations that come out of that analysis: real-time detection, the action it triggers, the monitoring that proves it still works, and the documentation someone else can operate it from. Work on the systems around our AI voice and chat agents across fourteen markets — automated conversation audits, the loop that turns an unanswered question into a knowledge base update, and the evaluation that says whether a prompt change helped or hurt. Design and read the experiments behind what we ship: choose the unit of randomisation, pick the metric that reflects the real outcome, and report the result honestly, including when it says the thing does not work. Build processes that send an input to an LLM and need a specific, reliable output back — together with the checks that confirm the output is what we expected. Hand findings to the right owner when they sit outside RTO, with enough evidence that they can act on them.