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A Fresh Perspective on Data Science: An Interview with Meghan Villard

We chatted with Meghan Villard, Manager of Data Science, about diversity, uncovering her passion, and being a square peg in a world of round holes.

“Whether we mean to or not, data scientists embed their life experiences, interests, values, into the way we analyze data,” says Meghan Villard, a Data Scientist with RevGen Partners.

Meghan is no stranger to being the square peg in a data science world of round holes. With a bachelor’s degree in Radio, Television, and Film as well as Mathematics, she’s always straddled the line between the creative fields and STEM. When she jumped into data science, though, her career changed trajectory.

“I always wanted to be in the CIA – to use math in an exciting way and help save the world. Except I can’t keep a secret! So, I had to find an alternate route. About ten years ago, when data science was an emerging field, it became apparent that this was a way I could take that investigative route.”

She earned her Master of Data Science from the University of Texas and then went into consulting, combining her two areas of expertise in Accenture’s Media & Entertainment division. From there, Meghan moved to unicorn startup Ibotta before landing at RevGen. Over the years, she’s watched the field of data science advance by leaps and bounds.

So, what excites her about the future?

“Democratization of data science – getting data science capabilities into the hands of decision makers. Automated Machine Learning (AutoML) is a great example of this. It’s a solution that is aimed at subject matter experts who may not know how to code, but they have specialized expertise close to the business problem.”

She continues, “At RevGen we have a framework for data science projects, one that focuses most on the first step, defining the problem, and the last step, using the output to drive value. Those steps still need human brainpower. AutoML just fills in the middle.”

Quote: Data scientists find meaning in data that’s in line with our understanding of the world. Because of this, it’s important to have diversity in data scientists.

For Meghan, finding success in data science always comes back to the human element. “It’s so important for us to put a name or a face behind the problems [we’re trying to solve]. When he was Chief Data Scientist of the white house, DJ Patil would determine their next project by reading letters sent to the president.

“For instance, he built Precision Medical Initiative, a massive database of rare conditions that teams can use to find correlations and take action. But that idea started with a note from a woman who wrote in saying she was sick and had visited over 20 hospitals for diagnosis and had no idea what to do next. His ‘business problem’ was helping this one person and [the solution] is still in use today.”

A mentor to young women through organizations such as Girls Who Code and the Girl Scouts STEM programs, Meghan has seen the passion the next generation has for data and STEM. While she’s excited for the next wave of scientists, she says it’s never too late for someone to get involved.

“Education in data science is easier than ever. You don’t need to invest in a master’s program right away. Start with an online course like Coursera or Udemy.” She suggests honing your data science skill set by finding problems to solve in your current career, or even opportunities that come up within hobbies or volunteering. Meghan emphasizes that real world connection in making her own jump to data science. “It’s when you can put a face to a problem that it becomes real! Then you’re motivated to jump in to help.”

Which brings her back to diversity. A study, conducted by Forbes in 2017, reviewed enrollment data at a technology college and highlighted the lack of diversity in both gender and race/ethnicity in the school’s Data Science program. Another 2018 study found that only 15% of working data scientists were women. The same study found that 45% of data scientists come from a mathematics or computer science background, just 8% from a business background, and only 5% from the social sciences.

“[Data scientists] find meaning in data that’s in line with our understanding of the world. Because of this, it’s important to have diversity in data scientists. Beyond gender, but where you’re from, age, education, professional experience, etc.” She adds, “This diversity brings different perspectives to the types of issues and where to start digging in.”



Meghan Villard, Manager of Data Science at RevGen Partners Meghan Villard is a Manager of Data Science at RevGen Partners.  She is passionate about empowering clients to make data-driven decisions that deliver value to their business.

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