Human olfactory perception is understudied in the whole spectrum of neuroscience, from computational to system neuroscience. In this study, we explore the hierarchy underlying human olfactory perception by embedding perceptual data in the hyperbolic space. Previous research emphasizes the significance of hyperbolic geometry in gaining insights into the neural encoding of natural odorants. This is due to the exponential growth of the hyperbolic space, that makes it appropriate to encode hierarchical data. We employ a contrastive learning approach over the Poincare ball in order to embed olfactory perceptual data in a hyperbolic space. The results indicate the emergence of a hierarchical representation in the hyperbolic space, which could have implications for understanding the structure of the olfactory perceptual space in the brain. Our finding suggests that the human brain may encode olfactory perceptions in a hierarchical manner, where higher odor perceptual certainty correlates with deeper levels in the hierarchical representation.