ReCAP: Josiah Green

Name: Josiah Green

Employer: Principal Financial Group

Job Title: Data and Operations Research Scientist

Primary Job Functions

• User Experience, Algorithm Design, Systems Engineering

Date CAP Certification Was Earned

July 15, 2019

How often do people ask you, “What does CAP mean?”

The first person who asked me was my wife, and not many people have asked me since. In the financial services industry, I think people assume this is a misspelling of CPA.

How familiar were you with the “Seven Domains of Analytics Process” prior to pursuing CAP?

These domains corresponded almost one-for-one to the focus areas of my industrial engineering undergraduate studies. I was very familiar with the concepts, but I appreciated how they are applied in the CAP methodology.

What have you loved about being a CAP?

Being a CAP gives a sense of credibility in the domain of analytics. I cringe when people claim to do “analytics” when in reality they are only visualizing a poorly constructed Excel workbook or misapplying out-of-the-box Python packages. Being an analytics professional is much more comprehensive – it’s as much about the “what” of our work as it is the “how” we do it.

What was your most valuable resource while preparing for the exam?

I used the practice exams in the study guide and CAP handbook.  

What has been your most interesting PDU, class or otherwise?

The most interesting PDU has been the Johns Hopkins Data Science Specialization. Part of the specialization was about Reproducible Research. This entails writing comprehensive step-by-step guides through an analytics process including the executing of code and generation of plots.

In your opinion, what will be the next "game changer" in analytics?

Artificial intelligence (AI) as it exists today in machine learning (ML) does not exhibit the type of intelligence that we expect of a human. We often find it the case that the outputs of today’s ML models seem intelligent, but these models cannot explain the causal path they took to arrive at a conclusion. The next game changer, in my opinion, will be explainable, causal-driven models – a computer that can explain the what (output) and the how (causal reasoning) it took to arrive at a prediction.