Urban-scale energy simulation relies on the understanding of occupants' presence in buildings and consequently in cities. Therefore, occupancy profiles (i.e., the relative number of occupants in a specific hour of the day) are usually used in the energy simulation on the city level. However, available occupancy standard profiles are incapable of considering the dynamic nature of occupancy schedules and any changes that occurred due to contextual changes (such as the dramatic increase in remote working last year). Therefore, the need for a scalable method to generate dynamic occupancy profiles for buildings is crucial. Moreover, the targeted method should allow for tracking the changes that occur in occupancy profiles due to external disruption such as pandemics. In this context, this study aims at using the emerging mobile positioning data to generate context-specific data-driven occupancy profiles for commercial and institutional buildings in New York City. The generated profiles were then compared versus ASHRAE standard profiles for each building category. Then, the occupancy profiles were clustered for each building category, using K-means clustering algorithm. Finally, the effect of COVID-19 pandemic on the peak points and shape of occupancy profiles was investigated. The results showed a significant difference between the data-driven and ASHRAE standard profiles. Additionally, a considerable variation in the shape and peak hours of the generated occupancy profile clusters was detected for some building categories. These results can be used to improve the accuracy of the urban-scale simulation models. Furthermore, they can provide a more precise evaluation of the occupant’s schedules and consequently the urban scale energy consumption before field implementation of the operational strategies.