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Dexory is seeking a data-oriented reliability-focused analyst to convert robot fleet telemetry into practical fixes. You will build dashboards, examine time-series data, and parse logs to identify repeat failures, ensuring consistent configuration across the fleet.
A few years in a data-heavy or reliability role, comfort with Python and time-series data, and the ability to write clear findings for engineers are key requirements.
This role turns robot and fleet data into practical fixes. The work covers building dashboards and alerts, digging through telemetry and incident logs for repeat failures, and keeping robot configuration consistent across the fleet. A strong candidate has a few years in a data-heavy or reliability role, is comfortable with Python and time-series data, can read Linux logs, and writes clearly enough that engineers act on the findings.
AI can help spot patterns in telemetry, flag unusual behavior early, and speed up the first pass through large piles of logs. The human part stays important: deciding which signals matter, judging whether a failure is a hardware, software or setup problem, and explaining the cost of an issue so the right team takes it on. Noticing what nobody reported still depends on curiosity and judgment.
The company runs robots that feed an AI digital twin of the warehouse, so this role sits close to the data that keeps that picture accurate. Someone here would learn how fleet health data is gathered and used, and how early warning signals are built from real machine behavior. Building that experience beforehand could mean practicing anomaly detection on time-series data and learning basic ROS log reading.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
Autonomous robots scan every pallet in a warehouse each day, per the company, and feed an AI digital twin, so a warehouse manager can see real stock levels instead of what the systems assume.