Women in Data Science: Advice From 4 Experts was originally published on Springboard.
What’s the day-to-day of a data scientist in 2019? What skills are needed to land a data science role? What trends should data scientists be aware of? We recently partnered with the GUILD, an AI-driven networking platform for women, to organize a panel talk featuring four fantastic women in data science. After the talk, we grabbed them for a quick round of questions.
Meet the panelists:
Emily Bailey, data scientist, strategic finance, at Uber: In addition to leading a data science group focused on financial optimization, Emily co-leads the Women in Data employee resource group.
Kathy Yang, data scientist on Airbnb’s strategy and insights team: After earning degrees in economics and mechanical engineering, Kathy’s winding path took her through a management consulting job and think tank research to data science.
Jana Dodson, senior data scientist at Shift Technologies: Jana’s data science career has taken her to a variety of industries, including healthcare, telecom, and automotive.
Lily Shen, product manager for data science and machine learning at Niantic, Inc.: Lily joined Niantic (the AR gaming company behind Pokemon Go) as the first data scientist and built the company’s data science foundation from scratch.
Have you dealt with imposter syndrome?
Emily: Imposter syndrome has been the majority of my career and I’m just now getting to a point where I’m realizing that we’re not all imposters, we’re all just talented in different ways in different parts of this field. My current team—I work on the finance data science team at Uber and we work on a financial optimization product, where we’re determining how we should spend money efficiently. I had never studied anything in this field and so I joined this team and I honestly wondered if they were speaking a different language at times. I was so in over my head. But today I’m leading that team. So, it’s a world where you just need to accept that there’s always growth.
Kathy: You don’t have to check every single box. I think especially in data science, which has such a broad scope, no one’s going to be an expert in everything. So there’s always going to be things you don’t really you understand or that you can do better.
Jana: Most data scientists have graduate degrees of some sort, so to me, I kind of wonder: how do they let me do this every day? But, you know, I guess you sort of prove yourself.
Lily: As long as, you know, your problem-solving framework is the right framework, you eventually reach the right conclusion. So, yeah, [I believe] in math and logic.
What are the core job skills for a data scientist?
Emily: There’s a set of basic skills that every data scientist will need to have, which is the ability to code in some scientific programming language, a good understanding of basic statistics and math. But from there, there’s a number of people who find that there’s one piece of this very large field that’s super interesting for them and they choose to specialize.
Kathy: I think one thing is communication because sometimes data is the product that you’re making—like if you have a new pricing algorithm and you roll it out—but oftentimes data is used to influence a product more indirectly or other business decisions, so you have to be able to describe what you’re doing… and actually try to convince people to see, you know, see in the data what you’ve seen.
Jana: Critical thinking skills—like, do you know how to assess a problem you haven’t seen before? Can you kind of reason through something if it’s new to you?
Lily: Being able to communicate clearly and being able to—almost like salesmanship, to be honest. To sell your idea to whoever’s in charge, be it the product manager, be it, you know, the team that you work with, be it the decision maker of the thing that you want to change.
Related: How to Become a Data Scientist
What are some upcoming industry trends?
Emily: I think there will probably be a trend toward very serious specialization—and we’re already seeing this. A lot of the really juicy problems out there require extremely specialized knowledge, whether it’s computer vision or natural language processing. So I would encourage people to find a little niche of the world that interests them and there will be opportunities in it.
Kathy: Just the types of tools that we use will probably become more advanced and more accessible. And along with that, I think it’ll become more important to really understand the math and the statistics behind what you’re doing.
Jana: There’s still sort of a gap that needs to be bridged between tools that data scientists like to work with and can easily translate their thoughts into versus tools that can work with very large data sets and work very quickly.
Lily: Although technical skills are still very important, I can see machines eventually replace humans in those technical skills in a speed that is really fast. For every assignment or project you do, to not only to deliver the project well, but also ask yourself: what did I learn or grow out of the project? It can be technical skills, but it can also be, you know, how does it change my view of the world or of the industry?
What advice would you give to your younger self?
Emily: The biggest piece of advice I have for anyone is to ask for help. When you have a goal in front of you, it can be daunting to see how big it is, so the first place I tend to ask for help is: how do I break this up into smaller pieces? And then, even if the first piece is hard, you can ask for help every step of the way if you need to.
Kathy: Not to stress out about every little decision. Oftentimes, your experience is something that could seem super unrelated but could come back and prove to be really helpful for you later. For example, while I was at McKinsey I did some research about smart cities and how technology can influence urban life. And, you know, at that time [I] maybe didn’t think I was going to be a data scientist, but in applying to Airbnb for their data science roles I realized that I did have a lot of this knowledge that was relevant.
Jana: To try a lot of things that you might not normally try.
Lily: Having just a technical skill is not enough, you know? To start training early on communication, on leadership and teamwork—they are all very important in actually succeeding in actual work.
Related: Data Science Career Advice for College Students
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