Transition-metal catalyzed organic reactions are essential to the synthesis of pharmaceutical, agrochemical, and fine chemical ingredients. For many such reactions, performance is controlled by the structural features of the auxiliary ligand attached to the transition metal. The relationship between the chemical structure of the ligand and the behavior of the catalyst is typically difficult to decipher intuitively. Consequently, reaction optimization is a resource-intensive, trial-and-errorbased campaign. Data science tools have recently emerged to streamline this effort through the development of methods to relate chemical structure to function. In particular, the calculation of descriptors has evolved to incorporate diverse, refined properties, which can be scaled to produce databases. The resulting descriptors can then be fed into a range of data science workflows to optimize a particular objective such as yield, reaction rate, regio-or stereoselectivity. These workflows include machine learning (ML) algorithms such as multivariate linear regression (MLR) to regress structure to function, classification tools to explore reactivity cliffs, and dimensionality reduction techniques to map chemical space to visualize reactivity patterns. More sophisticated algorithms can also be employed to explore the multi-dimensionality of reaction optimization including the exploitation of Bayesian optimization tools. However, limited effort thus far has been reported for the simultaneous optimization of multiple objectives. This is an underlying challenge in organic chemistry as high performance in one objective (e.g., yield) does not necessarily correlate with desired performance in another (e.g., stereoselectivity).