From studying extreme environments to using data to answer business questions, Tessa Jones from Calligo shares how her science-focused mind has helped her succeed in data science.
Tessa Jones, director of data science at Alteryx partner, Calligo, has had many amazing experiences in her career: first as a scientist exploring both earth and space, and then as a data scientist who emphasizes keeping the science in data science. She recently joined us on the Data Science Mixer podcast and shared how the scientific aspects of data science enrich her perspective and her professional life.
Here are three “principal components” of my conversation with Tessa.
1) The business is the ecosystem that the data scientist studies
It's critical to think about businesses as your field of exploration.
As we’ve progressed through how data science as a market and as a human experience is evolving, the science has been the one piece of it that's been a little bit wavering. People are confused about what it means. I've often wanted to refer to my team more as business scientists rather than data scientists, because it's like you think about different kinds of scientists, right? In another world, I would have considered myself maybe a chemist — a certain kind of scientist studying chemical interactions. And I really think it's critical to think about businesses as that. It's your field of exploration — thinking of it more in terms of the business being the ecosystem that you're studying and trying to improve.
2) The timing of projects and their implementation can be challenging for data scientists
It can end up meaning that your data scientists aren't optimally utilized.
One of the difficulties I've seen in companies that leverage data scientists is that they'll have a need, and they hire one or many data scientists. They're excited and they get it going. And then there's this downfall in need. It's this highly volatile process, and it can end up meaning that your data scientists aren't optimally utilized. They tend to have these lull times, and they're trying to figure out how to make things useful for the business. … A lot of times when you build something, you build it and you deploy it, but you have to give it a little time before you can even determine, "What are the effects of this? Did we do what we thought we did? Did it have the effect we wanted it to have?" You have to let it live a little bit of a life before you can really determine that.
3) When collaborating on data science projects with stakeholders, first understand your audience
You need to understand how they're going to relate to whatever it is you're going to develop.
One of the more critical pieces is to first understand who you're talking to. Have curiosity about the person themselves. Really, at the end of the day, you need to understand how they're going to relate to whatever it is you're going to develop. I've had lots of different stakeholders, some of whom don't want to know anything about the technical development. Some of them want to make sure they understand how all the business works. Some of them want to learn as we go. And I think it's really critical to first understand that because it really builds the relationship between you and the stakeholder. You're just going to end up with a better product, but you're also just going to have a better overall experience, I think. I would say that's the number one thing to do.