Sales professionals help organizations win clients for products and services. Generating new clients starts with identifying the right decision makers at the target organization. For the past decade, online professional networks have collected tremendous amount of data on people’s identity, their network and behavior data of buyers and sellers building relationships with each other for a variety of use-cases. Sales professionals are increasingly relying on these networks to research, identify and reach out to potential prospects, but it is often hard to find the right people effectively and efficiently. In this paper we present LDMS, the LinkedIn Decision Maker Score, to quantify the ability of making a sales decision for each of the 400M+ LinkedIn members. It is the key data-driven technology underlying Sales Navigator, a proprietary LinkedIn product that is designed for sales professionals. We will specifically discuss the modeling challenges of LDMS, and present two graph-based approaches to tackle this problem by leveraging the professional network data at LinkedIn. Both approaches are able to leverage both the graph information and the contextual information on the vertices, deal with small amount of labels on the graph, and handle heterogeneous graphs among different types of vertices. We will show some offline evaluations of LDMS on historical data, and also discuss its online usage in multiple applications in live production systems as well as future use cases within the LinkedIn ecosystem.