The data science ecosystem encompasses data fairness, statistical, ML methods and tools, interpretable data analysis, and trustworthy decision-making. Rapid advancements in ML have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem that integrates statistics, ML, and genomics and health science. I will illustrate key points using the analysis of large scale whole genome sequencing data, electronic health records and biobanks by discussing a few scalable and interpretable statistical and ML methods, tools and data science resources for gene mapping and genetic risk prediction. Examples of large WGS studies and biobanks include the Trans-Omics Precision Medicine Program (TOPMed), TOPMed, UK Biobank and All of Us. These studies have collectively sequenced over a million genomes and thousands of diseases and traits from electronic health records.