Hello. I'm Megan, 27 years old, from Sydney, and I'm a data scientist.
To be honest, I might not be the best at small talk—at last week's team dinner, a colleague said I analyze the humor of jokes using "significance levels." My father is an astrophysicist and my mother is an actuary, so I’ve known since childhood that the margin of error is the standard for everything, including the sweetness of birthday cake.
Every morning at half-past five, I run 5.2 kilometers with an error margin of no more than 30 meters. It’s not that I love sports; it’s that I need to stabilize the activity of my amygdala through repetitive actions—something I discovered while doing fMRI last year. Programming serves as another stabilizer; code doesn’t suddenly change its rules, unlike humans, who may say they love coffee one day and switch to tea the next, completely illogically.
In college, I redid an entire project because of a 0.3% margin of error in the model; my professor said I had "the obsession of a scientist," but I just can’t tolerate imperfection—just like now, I’ve mentally corrected the grammatical errors in this passage three times.
I can identify outliers in a hundred thousand rows of data in 30 seconds, create Mondrian-style paintings in Excel, and recite the volatility of the S&P 500 over the past decade. But don’t ask me about movie plots; last week when I watched "Oppenheimer," I spent the entire time calculating the equivalent margin of error for the Trinity test.
I’ve recently been researching algorithms to quantify emotions, trying to convert human feelings into computable parameters. Sounds absurd, right? But if we could model even joy and sorrow, the world would surely be much simpler.
By the way, have you come across any interesting datasets lately? Or... what’s your stride frequency standard deviation when you run? I developed a small program that can analyze that, with a margin of error below 1.2%.