In job-centric monitoring, monitors gather series of measurements, e.g., the used CPU load, per job. In domains where jobs are expected to behave similar, job-centric monitoring allows detecting misbehaving jobs based on a reference series of measurements. However, current detection approaches neglect time-drifts in series, e.g., caused by different CPU speeds and therefore potentially cause false positives.To cope with this issue, this paper introduces a novel approach to compensate such time-drifts. Our approach is based on a transformation that aligns a series of measurements to the reference series time. In a proof-of-concept with synthetic job-centric monitoring data, we show that our approach reduces the number of false positives significant.