Confessions of a data scientist: “Marketers don’t know what they are asking for”

Like artificial intelligence or blockchain, data science is a popular industry buzzword. Marketers are often confused about the distinction between data scientists – those who design and test experiments using statistics, calculations, linear algebra – and data analysts – those who use sheets of calculation to implement a strategy around the data.

In the latest installment of our Confessions series, where we trade anonymity for honesty, Digiday spoke with a data scientist from the marketing department of a company who says marketers are always at a loss when it comes to science and waste money on data scientists.

Do marketers understand data science?
Marketers don’t know what they’re asking for when they hire a data scientist. It’s the Wild West, especially if a company has never dealt with data. Companies that aren’t ready for a data scientist hire one and end up wasting $100,000 a year. Most small businesses don’t need a data scientist; they need someone to manage a spreadsheet or a data analyst. Marketers often don’t know if they want data analysts or data scientists and swap the terms. If you look at the variety of analyst jobs available on Linkedin, if a company is asking for someone who can work in Excel, they’re looking for a data analyst, but if they’re talking about R, Python, or machine learning, they’re looking for a data scientist, whatever he calls the job.

What confuses marketers?
Data science is such a vague and meaningless word in the industry. It’s a buzzword, and because it’s such a buzzword, it’s easy for anyone who touches data every day to call themselves a data scientist. There are many people trying to hack their entry. It’s like bitcoin. Everyone wanted to buy it. Everyone wants to be a data scientist right now, but you have to know what you’re doing. Even the word “model”. Depending on whether you’re talking to a data scientist or a marketer, you can be talking about very different things. It’s not just the industry’s fault.

What else feeds the confusion?
Schools springing up around us, like coding bootcamps, perpetuate the idea that you can do data science without knowing statistics. It makes no sense. Coding bootcamps will take people and put them through a course where they learn code formulas and then call them data scientists but they’ve never taken a course in statistics or linear algebra so they don’t know not what they really are. To do. Anyone can type LM into an R terminal and get a linear model, but that doesn’t mean they actually understand what a linear model does or how predictive it is.

How does this harm businesses?
There’s a mismatch between the extra level of complexity achieved at the data science level, which big companies are really looking for, and the kind of analysts pumped in by non-academic institutions. Whenever you find places that say how little math you have to do to become an analyst, they end up being pretty weak analysts. Anyone can look at the performance of any company over the past six months, take the average, and say the next month you’re going to do this. This is not a true prediction; that’s about as low as it gets and that’s the base analyst level they get. So if a company misses its KPIs month after month, it’s probably because its data scientist didn’t know as much.

Is it strange to be in a marketing department? Prefer to work in a tech company?
For me, I’m doing my job well if I can give my business and my stakeholders the data they need to make decisions. It’s a good thing to be part of it, no matter where you are. You solve a puzzle every day. How can I predict future behavior? It’s pretty cool. Throughout human history, people have loved fortune tellers.

Are you satisfied with what you are doing?
I get paid a little less than the median, but that’s because I have about five years less experience. Companies recognize how competitive it is, so it is a high-paying career. I make $90,000 and the median salary is $120,000 and could go up to $300,000.

Confessions of a data scientist: ‘Marketers don’t know what they’re asking for’

Sean N. Ayres