Receiver sensitivity is one of the most important performance metrics in communication systems. Industry has been using exhaustive search in a specified range to identify the sensitivity of a receiver. However, such an exhaustive search is neither efficient in terms of measurement time, nor accurate due to the fixed step-size. In this paper, a data-driven approach is proposed to measure the receiver sensitivity based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Unlike the model-based approach, the proposed data-driven approach is dependent only on the input and output measurement data. In addition, we derive the minimum number of test packets needed to satisfy the desired confidence level for packet error rate estimation. By adapting the number of test packets, we are able to further reduce the measurement time. Numerical analyses and experimental results show that the proposed data-driven sensitivity measurement can achieve good estimation performance as well as reduce measurement time.