On June 13, 2019 at DSSNYC, five incredible female data scientists took the stage at Viacom for the first Data Science Salon Elevate panel in history.
Though the panel was only 30 minutes long, the topics covered spanned from the different styles of mentorship and their purposes to the rapid installations of new tech in the media industry and the corresponding need for new data science talent.
Full insights from Lauren Lombardo, Senior Data Scientist at Nielsen, Noemi Derzsy, Senior Data Scientist at AT&T Labs, Harini Kannan, Data Scientist at Capsule8, Sophia Tee, Senior Manager of Data Science at Verizon, and panel moderator Anna Nicanorova, VP of Engineering at Annalect, are available in the DSSe Voices podcast episode below.
Noemi Derzsy, Senior Data Scientist at AT&T Labs, speaks at the DSSe Panel at Data Science Salon New York 2019.
The Role of Community in Career
For the panelists on stage, addressing an audience of over 200 data scientists seemed like the perfect time to talk about the importance of building communities - both online and offline. The group identified some noteworthy communities to join for women in data science such as “She Runs It” and “Ladies Get Paid.”
Seeking support outside of work can be refreshing and necessary to maintain a healthy perspective. However, Lombardo stressed that external support, though powerful, is not enough. “If you don’t have a strong internal community at the company for which you work, that’s something you should work towards,” she said.
Whether the sharing of ideas happens inside your work environment or outside, collaborating with others is the best way to hone one’s skill-set and avoid miscommunication between teams. “In a big organization, it’s easy not to know what other people are working on,” explained Derzsy. “Meeting up regularly is a way to see what’s happening with other teams.”
Sponsorship, Mentorship and Everything in Between
There are many different ways for a budding data scientist to receive mentorship within the industry and the panelists teamed up to form some important distinctions.
Lombardo focused on the benefits of pursuing both external mentorship outside one’s company and internal mentorship at the office. “External mentorship is very helpful to get an unbiased view and perspective on the industry,” said Lombardo. “However, when I’m thinking about how do I actually understand the ins and outs of my company and grow where I am, internal mentorship is essential for that.”
Lombardo also made the distinction between having a sponsor and having a mentor, encouraging the audience not to try and make one into the other. “Having a sponsor isn’t necessarily somebody that you will go to for candid advice or sharing areas of your insecurities,” she explained. “But if they know what you’re good at and what you’re looking for, they may be the person that gets you your next job.”
It’s unlikely that one mentor will serve all of the above purposes and in general, it’s good to seek out multiple advisors. Kannan found comfort from mentors that helped her learn the basic tools she needed to move forward at the beginning of her career. “Even though learning a new skill like Python is a steep learning curve, having someone who can sit with you and help you through that process is helpful,” she said.
According to Derzsy, many successful mentor relationships are not the ones you consciously pursue. “I had good mentors in my career and it wasn’t because I chose them. I didn’t know what I was looking for at that point,” she confessed. “The ones who became my mentors pushed me outside of my comfort zone and gave me constructive criticism, which nobody should be upset about because it’s how you’re going to grow.”
Sophia Tee, Senior Manager of Data Science at Verizon, speaks at the DSSe Panel at Data Science Salon New York 2019.
Lack of Knowledge is a Good Place to Start
Though blogs upon blogs are written about overcoming “imposter syndrome”, Lombardo thinks that such feelings can manifest as something positive. “Obviously, you don’t want to feel like you can’t do something so much that it prevents you from doing that thing. But overall, that feeling of apprehension can be illuminating because it shows that you care,” she said. “It is worse for me to be doing something I don’t really care about than being in a place that scares me enough to grow.”
Derzsy agreed and further implored the audience to choose external influences wisely. “You should filter out your friends and acquaintances that say you can’t do something,” she advised. “We all have doubts inherently and you don’t need to amplify that voice.”
Creating the Optimal Team
Many of the panelists’ roles at their companies also involve building a viable data science team. This responsibility does not come without its challenges and each panelist with hiring experience had her own take.
“Having a combination of both IQ and EQ is very rare and the ability to translate abstract concepts to something that everyone can understand is not a skill everyone has,” said Tee. “Communication is very key to what we do every day and of course, having a good technical background is a plus.”
Though it may be surprising that a technical background would be labeled as a plus and not the main qualification sought, most of the panelists agreed that other aspects of an applicant take precedence. At Nielsen, Lombardo said that she “often needs to pass on people that are good data scientist candidates for people that offer a unique skill-set that we need for specific projects.”
Kannan put it clearly and simply: “Culture fit comes before skill-set. Don’t look at the qualifications over what work they can show.”
Can You Make the Transition?
According to Kannan, “The fastest and easiest way to get into data scientist is by doing data science.” Plenty of open data is available for your perusal and many of these datasets come with free online tutorials that can walk you through how to use them.
“Do your homework, but then once you’ve done your research, trust yourself and just go for it,” advised Lombardo. “Obviously, one Python class is not enough to be a data scientist. But if you’ve put in the honest work, you need to take a step into the industry and believe you can move forward. Everybody in data science came from somewhere else. They had a different career path and then taught themselves the skills they need for data science.”
It’s easy to get lost in all the documentation and online courses available on the Internet. Fortunately, Derzsy has hope that it can be managed. “In academia, you can read an endless amount of books and never get to do any data analysis,” she explained from experience. “The way to learn the fastest is to get a problem, get the data and then push yourself to start playing with it.”
Can the World?
We’ve talked about how individuals can make a challenging but smooth transition into data science. But how will the world at large, specifically within the media and advertising space, change to accommodate an influx of data? According to Lombardo, the major question is: “How do we serve addressable ads within traditional TV content?” Traditional mediums may stay on the scene but the way they go about collecting and presenting their content could be disrupted by data science.
Tee assures budding data scientists that now is as good a time as ever to dive into the industry. “With 5G, the volume of data being collected and the speed at which it will be collected will create an explosion of information that will allow data scientists to come in and create value,” she explained. “This is the best time to get into the industry.”
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Join Us in Miami
If any or all of these events sound appealing to you, we request that you visit the DSSe page to watch more content and see what we’re all about.
More importantly, we want to see each and every one of you at DSS Miami on Sept 10-11, 2019 at CIC in Miami. The best and brightest of the data industry will engage you with current applications of AI & Machine Learning To Finance, Healthcare & Hospitality specifically and you’ll get the chance to network with experts and fellow practitioners throughout the day and after the talks at happy hour.
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This post is part of DSSe, an initiative to elevate the voices of women in data science.