We study fairness of scoring in online job marketplaces. We focus on group fairness and aim to algorithmically explore how a scoring function, through which individuals are ranked for jobs, treats different demographic groups. Previous work on group-level fairness has focused on the case where groups are pre-defined or where they are defined using a single protected attribute (e.g., whites vs blacks or males vs females). In this manuscript, we argue for the need to examine fairness for groups of people defined with any combination of protected attributes (the-so called subgroup fairness). Existing work also assumes the availability of worker's data (i.e., data transparency) and the scoring function (i.e., process transparency). We relax that assumption in this work and run user studies to assess the effect of different data and process transparency settings on the ability to assess fairness. To quantify the fairness of a scoring of a group of individuals, we formulate an optimization problem to find a partitioning of those individuals on their protected attributes that exhibits the highest unfairness with respect to the scoring function. The scoring function yields one histogram of score distributions per partition and we rely on Earth Mover's Distance, a measure that is commonly used to compare histograms, to quantify unfairness. Since the number of ways to partition individuals is exponential in the number of their protected attributes, we propose a heuristic algorithm to navigate the space of all possible partitionings to identify the one with the highest unfairness. We evaluate our algorithm using a simulation of a crowdsourcing platform and show that it can effectively quantify unfairness of various scoring functions. We additionally run experiments to assess the applicability of our approach in other less-transparent data and process settings. Finally, we demonstrate the effectiveness of our approach in assessing fairness of scoring in a real dataset crawled from the online job marketplace TaskRabbit.