Showcase Projects

Royal Monetary Authority Bhutan – Data Warehouse and Analytics System

The Royal Monetary Authority (RMA) is the central bank of Bhutan and performs all functions related to central banking and currency control through 11 departments. The Datawarehouse and Analytics System involved a thorough data science study of Bhutan’s Central Bank, and produced numerous recommendation of a robust, sustainable, and scalable technology led data ecosystem that now aids RMA in performing advanced data-driven monetary policy decisions.

Data Scientists from Data Scientists Dot Com performed an extensive As-Is and Envisioned State analysis for RMA with key recommendations for the integration and utilization of existing technologies that will give RMA the ability to leapfrog into a level playing field of the fast paced FinTech ecosystem.

National Caner Institute, AsiaLymph – AI assisted epidemiology study

The National Cancer Institute has performed an advanced epidemiology study in the fight against Non-Hodgkin lymphoma (NHL). NHL has been increasing world-wide in developed and developing countries. Large-scale molecular epidemiological studies in North America, Europe, and Australia have studied the etiology of NHL but limited research had been done in Asia. AsiaLymp was the first large-scale international hospital-based case-control study of lymphoma among Chinese in Eastern Asia and was launched in 2012 to replicate and extend novel observations made in studies among Caucasians in a population that is distinctly different with regard to patterns of key environmental and occupational risk factors and genetic loci. This study employed next-generation of AI assisted occupational exposure modules, combined with historical exposure measurements on key exposures.


Data Scientists from Data Scientists Dot Com provide expert advice to the AsiaLymph study and implemented AI assisted technology for performing data collection. This system incorporated an occupational history data collection process to ascertain occupational, family, medical, and residential histories. Using a dynamic rules-based expert system, specific exposure survey modules were triggered in response to certain combinations of industry and job title for certain types of occupational solvents (e.g., benzene, OCs, TCE).


Data Scientists Dot Com provide key services in the implementation an support of this technology infrastructure which consequently decrease the time between end of data capture and publication by more than one year.