Changepoint Detection methods aim to find locations where a time series shows abrupt changes in properties, such as level and trend, which persist with time. Traditional parametric approaches assume specific generative models for each segment of the time series, but often, the complexities of real time series data are hard to capture with such models. To address these issues, in this paper, we propose VAE-CP, which uses a variational autoencoder with self supervised loss functions to learn informative latent representations of time series segments. We use traditional hypothesis test based and Bayesian changepoint methods in this latent space of normally distributed latent variables, thus combining the strength of self-supervised representation learning, with parametric changepoint modeling. This proposed approach outperforms traditional and previous deep learning based changepoint detection methods in synthetic and real datasets containing trend changes.