Love in numbers
The now 23-year-old McKinsey fellow data scientist grew up in what she describes as a 'knowledge thirsty' family. Both parents are professors (mum, of math, and dad, of psychology) who fostered curiosity within all aspects of life for Daisy and her older brother. Look no further than the dinner table where The Age quiz was a nightly activity and the toughest questions were fuel for conversation and debate.
In this home, the kitchen was used to assemble meals and ideas. Mum kept a whiteboard on the wall to note down formulas and proofs that came to her between seasoning and stirring. Walking past the scribbled hieroglyphs got Daisy intrigued, but it was Mum's suggestion to apply probability to a teenage drama that really got her attention.
At 15, that drama naturally centered on a crush. Daisy couldn't decide if, when, or how to ask a boy out. Mum skipped the usual pros and cons list to draw up a probability tree. Together they plotted out various scenarios, potential outcomes, and likely levels of happiness were each to play out.
With the highest chance of success and happiness Daisy took her moment at an upcoming party. They dated for two years.
The probability of having all the answers
Experiments like this gave Daisy an appreciation for the power of numbers. What was once abstract was now very real, though not everything came as easily as that first date.
The excitement of university meant Daisy missed a few foundational modules in her studies. In identifying gaps in her knowledge, she decided to apply herself and seek help, buying other students a coffee or lunch for tutoring. Daisy created a foundational habit on the setback, which was to never be afraid to ask for help and lean in to learning.
"I'm very comfortable not having all the answers; less so in wasting anyone's time. On a client project, I take lots of notes throughout the week and schedule up to an hour on Fridays to run through all my questions with colleagues. This way I'm starting each week and project more confident and knowledgeable than the last," she says.
"It's important for me to stay at the forefront of concepts and techniques within data science. The world of academia was great for this, but my corporate internships revealed a gap in my real world application. I feel I have the right balance now. At QuantumBlack I'm learning from the people who made a boat to fly with artificial intelligence to help Emirates Team New Zealand defend the America's Cup. It doesn't get much more cutting-edge or exciting than that."
Daisy is now sharing her own knowledge, presenting to clients and the data community on Bayesian networks which calculate probability by defining the relationship between variables. A common example is the relationship between diseases and symptoms - when someone has observed symptoms, the probability of the person having certain or various diseases is derived via a network of data, such as other cases in the area.
Always back to the drawing board
Mum's kitchen whiteboard scrawl gave Daisy an appreciation for written proofs
"My first project at McKinsey was to help solve a convex problem, which means there's one optimal solution. A decision engine was built in Python, but I couldn't see a story in the code. I couldn’t really understand possible solutions. So, I went through the code line-by-line, with pencil and paper, to understand how the variables were interacting," Daisy says.
"It's important for me to go back to first principles, strip back and lay out the problem, and understand the key facts and variables. Whether it's a client problem, personal challenge, or math formula, there is a positive to every negative, an opportunity to every challenge. I've made a habit of trying to understand and use both."