Sellers work together as a team on sales opportunities, using their expertise in different roles to increase the probability of a win. These roles include managing the relationship with the client, overall architecture support or deep knowledge of a particular product depending on the seller's expertise, and the current opportunity requirements. Forming the right team for an incoming opportunity is vital and depends on several factors including understanding the required roles for the opportunity and assigning the right person to fulfill these roles, taking into consideration the seller's social network. In this paper, we present the Opportunity Team Builder solution, which supports sellers in this work by dividing the process into the following sub-tasks; identifying the required roles for the opportunity based on the products that the client is interested in, recommending the best people to fulfill these roles, and providing a win probability figure to guide users in team formation. This supports the sellers in forming the bestfitting team for current opportunity dynamics. Each task in the solution is implemented as a model using historical data from previous sales opportunities. Models work in coordination with each other to ultimately maximize the probability of win over loss. The solution not only recommends the best person to join a team taking into account a combination of inferred skills and social relationships, but also the predicted impact the person can have on the overall performance of the team. We present how the whole solution is realized with an intelligent user interface enabling interaction with the user throughout the team formation process. Substantial experiments with real world data show that win/loss prediction is performed accurately and the Opportunity Team Builder solution can recommend teams that achieve a higher win probability.