The first thing you should know about Carla Gentry is that she calls herself a data nerd. If you don’t believe me, check her Twitter handle (@data_nerd) and her 55k loyal followers. The next thing you should know is that she’s built a career in data science from the ground up by pushing against the glass ceiling every step of the way.
And she’s come a long way – from a 20-something single mom living in poverty to one to becoming of the most important data science influencers. Gentry has worked with major corporations, taking their data, applying it to their business needs and producing insights that propel businesses forward.
She knows what it’s like to be the only woman on the team and is happy to share her hard-won knowledge with an ever-growing number of female data scientists. She recently shared with us some sage advice for women entering or interested in advancing in the field.
(PARAGRAPH - Avenir Light S) to promote asset. Keep short. Use trigger words like download, free or new.
(PARAGRAPH - Body copy) Two to three sentences about the asset. Think about ways to entice the reader to read more - what you learn from the asset and why they should click. This should be less than 50 words. This is only 40.
SAS Insights: What led to your interest in data science, and why have you pursued it?
Gentry: When I had attained enough credit hours to graduate with a mathematics degree, I had an "umm ..." moment where I thought: "What the hell am I going to do with a math degree?" [laughs] And my economics professor advised me to take a look at econometrics and said if I added an economics degree, I could work in any business gleaning insights and discovering answers to all kinds of questions.
Because my natural curiosity has always been my biggest weakness and strength, I was all for being able to use what I had learned to help businesses succeed and not worrying if academia (teaching math) was right for me or not.
My first job right out of college was at Ronald J. Krumm and Associates in Oak Park, IL. Their biggest client was Discover Financial Services. Getting to play with terabytes of data before anyone had heard of "big data" was a dream come true. I talked one of the IT guys into teaching me (I bribed him with food!) how to pull my own data so I didn't have to wait for someone else to do it for me. [laughing] The rest is history!
Remember, if you ever need a helping hand, it’s at the end of your arm. Audrey Hepburn
On break molds
SAS Insights: When you began your studies was it unusual for a woman to be entering this field? What was that like?
Gentry: I was one of five students in my advanced math class, all boys of course, except me. It was a man's field for sure. And it was hard, but I had people who believed in me. So I kept my spirits high, and even when asked to make coffee, I never let anyone see that it hurt. You had to have thick skin back then for sure.
I never dreamed about success. I worked for it. Estée Lauder
SAS Insights: What opportunities do women have in data science today that you wish you’d had when you began your career?
Gentry: They have a lot more options than I did back in 1998. But it's still a male-dominated field. Slowly but surely, diversity will win!
Science is not a boy's game. It's not a girl's game. It's everyone's game. It's about where we are and where we're going. Nichelle Nichols
On math, science and blowing stuff up!
SAS Insights: It's a pattern that young girls have a high interest in STEM, but that interest wanes around age 12. What do you think schools can do to keep girls interested in science and math?
Gentry: Making it fun. As a teacher, I would show real-life examples of how math was used. Making volcanoes erupt isn't just for boys!
Science makes people reach selflessly for truth and objectivity; it teaches people to accept reality, with wonder and admiration, not to mention the deep awe and joy that the natural order of things brings to the true scientist. Lise Meitner
On finding your passion
SAS Insights: We're seeing more high-profile women data scientists breaking down gender and diversity barriers. Are you following the work of any in particular?
Gentry: Well, if you asked a lot of women data scientists who their inspiration was, I believe some of them would say me, but I've always been a big fan of Caitlin Smallwood, the Vice President of Data Science and Engineering at Netflix. She started her career about 10 years before me, and I would admire her when I heard her speak about analytics. You could hear the passion in her voice.
To handle yourself, use your head; to handle others, use your heart. Eleanor Roosevelt
On the steadfastness of spirit (and math)
SAS Insights: What advice do you give to women entering the field?
Gentry: Have confidence and stand your ground. Do not let anyone tell you that you can't do something when you know you can! You have as much right to be where you are as a man, so never sell yourself short. And remember, math will never let you down. Math! Math! Math!
I used to not like being called a woman architect: I'm an architect, not just a woman architect. Guys used to tap me on the head and say, “You are OK for a girl.” But I see the incredible amount of need from other women for reassurance that it could be done, so I don't mind that at all. Zaha Hadid
On learning by trial and error
SAS Insights: You have said in the past you crashed many a server in your day and learned from each experience. Do you think female data scientists feel it is OK to make mistakes? Or is there still a perception that women have to work that much harder and smarter to be accepted?
Gentry: [laughs] Yes, and absolutely! That is how we all learn. Why should women be different than men in the mistake department? Do I think they feel they have to go above and beyond to outdo the guys? Absolutely.
I was taught that the way of progress was neither swift nor easy. Marie Curie
On standing up and being noticed
SAS Insights: Depending on the source, it appears that women represent anywhere from 15% to 50% of analytics professionals, but no matter the true number, women lack visibility. For example, many of the industry influencers are men. How can we change that?
Gentry: I would have to look at the data to prove to myself that we are fifty-fifty in the analytics profession. Even if the numbers are correct, we get paid less, get less for speaking and get less recognition than men. We still have a lot of work to do for sure. So how many of the industry influencers are men? Most of them. And most will follow a man before a woman any day. But, again, that is changing. Panels are taking notice, and you see a few women here and there, which is an improvement over the last 20 years!
Don't let anyone rob you of your imagination, your creativity, or your curiosity. It's your place in the world; it's your life. Go on and do all you can with it and make it the life you want to live. Mae Jemison
(PARAGRAPH - Avenir Light XS) About the Author(s)
(SMALL) George Schoolman, Ph.D. is credited with the discovery of rare medicinal compound found only in the Brazilian rainforest... If an author note or bio is necessary, it is recommended to do so at the bottom of the article as the last piece of the body of the article. Photos/headshots are not recommended. Cras dapibus. Vivamus elementum semper nisi. Aenean vulputate eleifend tellus. Aenean leo ligula, porttitor eu, consequat vitae, eleifend ac, enim. Aliquam lorem ante, dapibus in, viverra quis, feugiat a, tellus. Phasellus viverra nulla ut metus varius laoreet. Quisque rutrum. Aenean imperdiet. Etiam ultricies nisi vel augue.
- Reimagine marketing: Today, tomorrow and in times of disruptionPutting the customer first has never been more important than it is now. One way marketers can prepare for the new reality is to look at each step in the marketing process (the marketing lifecycle) and map martech capabilities into the lifecycle, based on what you are trying to accomplish with each step.
- Optimizing well placement to eliminate water poverty How data visualization is helping Water for Good bring fresh water to the Central African Republic.
- ModelOps: How to operationalize the model life cycleModelOps is where analytical models are cycled from the data science team to the IT production team in a regular cadence of deployment and updates. In the race to realizing value from AI models, it’s a winning ingredient that only a few companies are using.