Modern applications running on supercomputers have increasing demands for memory, in terms of capacity, speed, power efficiency, and persistence. Since there is no single memory technology today that can satisfy all these requirements, next-generation supercomputers are expected to feature deeper memory hierarchies that consist of characteristically different memory technologies complementing each other or working side-by-side. A central question in these scenarios is how to place application data structures on the available memories to achieve optimal performance. Manual allocation is prohibitive for applications with more than an handful of memory objects. In this work, we present an algorithm for data placement on hybrid-memory system based on a set of memory allocation rules and global data placement decision algorithm. We propose a tool OracleHMS that implements our algorithm and provides recommendations on code change and memory setup to programmers. Our experiments on real hybrid-memory system show that our tool recommendations match or outperform manual optimization in a variety of scientific and data analytics applications.