Data science has become increasingly popular as a methodological approach for advancing theory and practice. However, significant concerns exist as to how bias can manifest within data science projects. Using a gender and leadership project as an example, we discuss how and when bias can emerge through the life cycle of a data science project. Specifically, after acknowledging potential structural biases, we identify and examine four key stages where bias is likely to emerge: (1) bias in the representation of data and the labeling process; (2) bias in algorithmic modeling; (3) bias in causal inferences; (4) bias in interpretation and application of results to inform policy and practice. We discuss potential structural barriers for women in the workplace and then highlight four key stages at which bias may emerge during a project on gender and leadership. The four key stages include (1) bias in the representation of data and the labeling process; (2) bias in algorithmic modeling; (3) bias in causal inferences; (4) bias in interpretation and application of results to inform policy and practice. On each occasion, we present solutions aimed at reducing the occurrence of such biases. This review makes at least three major contributions. First, this article is among the first to highlight the full flow of a data science project. Second, we describe how, even after the completion of the project, bias can manifest when evidence is used to inform policy and practice. Third, this article provides actionable solutions that can aid both novice data scientists (i.e., researchers looking to use such approaches for the first time) and more experienced scholars. While we use gender and leadership as a running example, the primary aim is that the recommendations are relevant to all areas of research. Additionally, it is important to note that as data science and machine learning continue to evolve and change over time, so will the appropriate strategies to handle bias within these domains
