Title: Effective Self-Supervised Transformers For Sparse Time Series Data. Abstract: Electronic health records (EHRs) typically contain a wide range of time series data that is characterized by high sparsity and irregular observations. Self-supervised Transformer architectures have shown outstanding performance in a variety of structured tasks in natural language processing and computer vision. However, their use in modelling sparse irregular time series with tabular data has not been widely explored. One of the major challenges is the quadratic scaling of self-attention layers that can significantly limit the input sequence length. In this work, we introduce TESS, Transformers for EHR data with Self Supervised learning, a self-supervised Transformer-based architecture designed to extract robust representations from EHR data. We propose an input binning scheme that aggregates the time series inputs and sparsity information into a regular sequence with fixed length, enabling the training of larger and deeper Transformers. We demonstrate that significant compression of EHR input data is possible without sacrificing useful information, likely due to the highly correlated nature of observations in small time bins. We then introduce self-supervised prediction tasks that provide rich and informative signals for model pre-training. TESS outperforms state-of-the-art deep learning models on multiple downstream tasks from the MIMIC-IV and PhysioNet-2012 EHR datasets.