- I pick up my pen and write down what I see on the screen: 600 out of 14000 rows are selected based on the criteria A = 2 (that’s 4%); if I switch over to A = 5, 135 out of 14000 are selected (that’s only 1%) — I now know the probability of several outcomes based on two possible inputs. I do this a few more times and determine the probabilities of several more outcomes based on more inputs, and in the process understand the data much better. I document this so my future self, and others who may use the data, are aware.
- Salesperson A should have a overall sales target 10% less than Salesperson B. Given sales targets for multiple products, I have to determine how much to allocate to Salesperson A and how much to allocate to Salesperson B. I make a few notes, writing down in algebraic terms the relationship between the total sales target, the sales target for person A, and the sales target for person B. I perform rudimentary simultaneous equations and input the formula into Excel, quickly calculating the breakdown for each of the products.
- The variable component of a salesperson’s salary is 30%, and 30% of that is determined by subjective factors, input by the line manager; the rest is determined by performance against sales targets. I create a spreadsheet formula that allows the line manager to rate the salesperson from 0-100%, automatically adjusted to fit the weights of the variable component and subjective factors.
I had such a hard time trying to figure out why I needed learn math. That’s why sometimes work before school makes sense.
I love to read and write. Professionally, data science, technology, and sales ops are my thing. In my non-professional life, I aspire quite simply to be a good person, and encourage others to do the same. For those who care, I test as INFJ in the MBTI.