Scientific and management challenges in the water domain are multi-disciplinary, requiring synthesis of data from multiple domains. Many data analysis tasks performed by water scientists are difficult because datasets are large and complex; standard formats for common data types are not always agreed upon nor mapped to an efficient structure for analysis; and water scientists generally lack training in scientific methods needed to efficiently tackle large and complex datasets. This project is advancing Data Science and Analytics for Water (DSAW) by developing: (1) an advanced object data model that maps common water-related data types to high performance Python data structures based on standard file, data, and content types established by the CUAHSI HydroShare system; and (2) new Python packages that enable scientists to automate retrieval of water data, loading it into high performance memory objects, and performing reproducible analyses that can be shared, collaborated around, and formally published for reuse. This poster was presented at the National Science Foundation CSSI PI Meeting in Seattle, WA February 13-14, 2020.