Transitioning from Marketing to Data Science (alias “I get knocked and down and get back up again”)

Benötigte Lesezeit: 5 Minuten

Shannon Kehoe
Shannon Kehoe

Shannon Kehoe is a former Marketing Analyst who transitioned into Data Science. The data enthusiast shares how an open mind, networking, and clear, personal goals boosted her career. Having worked on both sides, Shannon also debunks common misconceptions and elaborates on how tech and marketing teams can collaborate successfully. And how tech companies can support a culture that embraces diversity and new ideas.

I picked two of her statements on how she made her way from marketing to data science to provide a glimpse on her interview. Listen to the full episode on Spotify. 

You managed to make this transition from marketing into analytic analytics on another struggle. Can you elaborate a little bit on how did you manage this transition so what helped you to get from marketing to data science?

So big plus I had working for me was I had, I went to the Georgia Institute of Technology, which is a school for engineers, primarily. And so all of my friends at university were engineers, you know, other than one or two. And so you had to learn how to talk about Python you had to learn how to talk about regression, you know, in some of these other things, if you wanted to make friends.  I kind of came out of undergrad, knowing how to talk the talk but couldn’t quite walk the walk. I also had a minor economics from Georgia Tech again, so I already had a little bit of quantitative ability, tucked into my CV there. So, I had a credential, you know, working in my favorite there. So that’s a little bit of a unique advantage I had, but I’ve seen plenty of other political scientists make the same career move I’ve seen plenty of other marketers make the same career move.

What really helped me was when I was on the marketing team and my first job was in Adobe analytics every day, pulling reports, talking to data scientists about like – okay how does the data get in here? You know? How does it end up in this nice clean little format that I then export and put into Excel, right? So be really curious and going out of your way to this might be a bit American again but going out of your way to grab coffee with some of these other teams right and be curious about what they’re doing. Because there’s, there’s always a breakdown between the tech teams and the business teams, and the tech teams are just as eager to fix that breakdown as the business teams are and marketing teams. So, going out of your way, it’s like: Hey how does this work? You can pick up some of that knowledge that you can then apply at your day job, where you can use it to build something for GitHub. So that was a strategy that I used a lot.

I made it really clear to management, you know, from day one, that data science was where I wanted to be in three years. And a lot of my bosses went out of their way to help me with that as well. When I was a Senior Analyst, I went and recruited mentors for me and made sure that I had the mentorship and the career advice that I needed to get where you want it to go. So that’s another point.

And then the last point was, don’t get defensive, or, or seem surprised or downplay what you’re doing in marketing. Marketing uses data as well. I mean, marketing is not going to get funding for a TV spot, or for a radio spot, or for PAD podcasts, you know blurb, unless there’s data that says the podcast trends with our target audience, and the TV spot runs at, you know the time that single moms are watching right after the kids are going to bed. And that’s our target audience. So marketing is data driven. But there’s a slightly different balance for the skill sets of data scientists have to have a trio right computers, computing stats, and business and they have a little bit of all three basically. And marketing focuses more on: okay how do I put together the marketing plan? How do I reach people? And they use data to make those plans, right? And to make sure that those plans work.

So at the beginning, I would get a lot of data scientists, data scientists or engineers look at me and go: Hey, you know, you’re from marketing. What are you doing ?  I wouldn’t flinch, and I’d say: What do you mean I’m from marketing? I have to use data too! This is not, and I turned it back on them, and I’d say like: I don’t know what you’re thinking. You’re crazy. I use data just as much, maybe not as in sophisticated fashion. You know the math might not quite be there yet but I can’t just wander around and get a million dollars for something without showing why.

What is the most common misconception about marketing that you encountered while being on your journey?

The biggest misconception that I’ve seen is that data scientists and engineering tend to think that these, you know, data science tools and engineering tools just live in the engineering and tech teams. And that’s not true. I’ve actually found hard bugs to solve, reading, writing, Excel formulas and some of the stuff I’ve seen in Python. Right. So there’s this idea that you have to pass kind of certain intelligence part to go into engineering and if you don’t go into marketing to put it perfectly bluntly, and that’s totally true. Like, that’s not the case, you know, I work with engineering teams, sometimes they come into the room and they already think, okay, we’re better than the business team. The business doesn’t know what they’re doing.  Neither team is going to be successful. Your engineering team will cut funding because you can’t communicate why you need what you need. For example, we need more time on Amazon’s cloud services, then you know the marketing team needs a TV spot or something, right? You will never be successful if that is how your engineering leader walks into a meeting with a business professional, and that really is the most common misconception that I have seen.


Tina Nord ist Marketing-Expertin, Autorin und Sprecherin. Die Kommunikationswirtin beschäftigt sich seit mehr als zehn Jahren mit Content Marketing. Seit 2016 erforscht Tina den Einfluss maschinellen Lernens auf Content und engagiert sich für die Repräsentation und Beteiligung von Frauen an der Entwicklung von KI.

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