This month’s we sat down with Principal Data Scientist, Ray You, for our Day in the Life chat. Ray tells us the questions he asks to understand the business context when building measurement solutions and gives budding data scientists advice on how to get started in data science.

What led you to a career in data science?

The story goes back to my time at university in Canada where I studied marketing. After I graduated, I tried to get a creative marketing job in Canada, but it proved quite difficult. Roles there require excellent English and a strong understanding of local culture. Having only recently moved to Canada from China, these weren’t skills I possessed yet. Instead, as I have always been a scientific and logical person, I took a curveball and went into marketing analytics.

Eventually, I moved to London in digital analytics where I got more in depth with digital advertising. Then I became heavily involved in A/B testing as a product analyst. Everything shifted when I moved to a role at Unicef. It was here that I moved into data science as I looked to face marketing challenges with modelling work. I’ve brought all the knowledge into my role as principal data scientist at MI Media!

What does a typical day look like for you?

There are three core elements to my role. Firstly, I spend a lot of my time talking to people. Taking the time to get the basic questions right and understand the business context and expectations. The more time I’ve been in analyst roles, the more I’ve found that understanding people and their context is vital to success. Once I have all the information I need, I will come up with a custom-built measurement strategy.

Next, there’s the model-building part of my role. This is the most technical area, considering statistics, coding and pipelines. Finally, and perhaps most crucially, is sharing the results. The people I deal with have a varied understanding of statistics, so I need to explain the rationale in plain English. I need to make sure they understand why I did what I did, why it makes sense and what in means for the business.

What mistake have you learnt the most from?

Early in my career I was too geeky and focused on technical solutions rather than business actions. I was part of a segmentation project that failed because the outcome constraint was not defined well enough when I started the project. The results were unusable. This experience taught me that, in any project I’m building, I need to understand what the business is trying to achieve. It goes back to my earlier comment about being a consultant. I need to know what the business can and can’t do on the back of any results I give them. Without this, I could easily make a fancy technical solution that, from a business point of view, adds very little value.

Who’s your role model and why?

I take strengths from a variety of different people. From the power of honesty and transparency from previous managers to people speaking in the industry about using unconventional ways to extract new insights. I have also learnt from my senior colleagues at Unicef the importance of not assuming that you know everything, even if you’re in a senior role. They understood that the colleagues in local markets had the best local expertise and trusted them to deliver. Finally, my peers in the analytics field such as the data director at Huel, Bhav, have given me examples of the right blend of an analytical mindset with great storytelling.