Decision analysis balancing both data analytics and human gut feeling is critical in designing efficient routes to synthesize new, complex small molecules. This challenge is faced by any organization seeking to deliver modern pharmaceutical compounds to patients in a prompt manner. In this presentation, we highlight the incorporation of data science approaches using R to develop metrics that aid in the development process current complexity, risk quantification, and process efficiency forecasting. Current complexity is a metric established from human insights that assesses a molecule’s complexity in the context of capability, tracking the ‘current’ complexity of a given molecule over time and enabling the quantitative assessment of a new route or process. Risk quantification utilizes a Bayesian framework to quantify risk from real data and operational patterns, at both the project and portfolio level, for assessing the delivery risk of early candidate nomination assets in areas such as FTE resource modeling. Process efficiency can be estimated with a predictive analytics framework capable of quantifying the probable efficiency of a proposed synthesis or benchmarking the outcome performance of the developed process, thereby minimizing the environmental impact of pharmaceutical production. These strategies have been effectively used to aid the decision-making processes for pharmaceutical R&D.