Delve into the trenches with a pragmatic guide to implement quantitative risk management. Gain knowledge of methods for quantitative program design that comprise risk primitives, analysis approach, and workflow design. Risk primitives such as capacity, appetite, tolerance, and KRIs are described. Understand what modifications can be made to simplify operational use of FAIR for first timers and how to embrace Python and R for analysis with an open source approach. Be empowered to address workflow challenges using a simplified approach to the entire risk lifecycle from assessment intake and management to modeling and reporting output and finally risk decisions with trending and ROI analysis. Additionally, learn implementation and operation of the program design through people, process, and technology. Finally, close the gap for the last mile of transition to quant risk management and learn how to communicate and report risk from the boardroom to the team room.