My first industry job, I worked as a Biostatistician at the National Health & Medical Research Council at the Clinical Trials Centre in Australia. The work was very challenging as I was working with real data, and even more stressful, I was working with patient data.
As a Biostatistician, I had to work closely with the oncologists, surgeons and doctors, and determine the Statistical Analysis Plan for the Clinical Trial. Will the Trial be a Double Blinded or Triple Arm Study, Will a Placebo be used, How many patients do we need on the trial, What are the inclusion and exclusion criteria, When will we stop the trial, etc. All these questions were answered using rigorous statistical techniques based on clinical knowledge and historical study results.
I would often share my solution with my manager, Val Gebski, who too often challenged my solution by throwing a lot of “what if” scenarios and getting me to think on several possible outcomes. We often spoke and debated for hours, sometimes the whole day, about different approaches that could be taken.
I remember some nights, in the middle of my sleep, I would get up to write out a method to use the next day to justify my solution. And, first thing in the morning I would run my method on some data and be very excited when the results agree with the typical method and in this way validates our new innovative approach.
I have many examples that I can share but I would say that problem solving is what I enjoy as it challenges me to think of new methods on how to approach a given problem.
I strongly believe and have seen that statistical/analytical techniques are powerful in producing repeatable and consistent results that are accurate when validated and, in a nut shell, Analytics is all about understanding a problem, understanding the relevant data that can help you solve the problem, asking lots of questions and then applying statistical/analytical techniques to make comparisons, predictions and finally optimizing your solution.