This paper studies the collaboration of talents on an online crowdsourcing platform. We use a structural matching model to capture the differentiated incentives of participants behind collaborations, with specific focus on incorporating incomplete information and competition into matching games with unobserved transfers among participants that affect the equilibrium matching outcomes. We estimate our model using data from a leading data science competition platform and recover the heterogeneous preferences of participants that determine whether and with whom they form teams. Using model parameters, we conduct policy experiments to investigate how the matching efficiency is affected by the information structure and the reward structure on the platform. Our results provide implications on how policy makers could better align individual incentives to foster and improve the performance of collaborations in a competitive environment.