8 Austin data scientists share how they got in the industry — and how you can, too

Kelly Jackson

IBM recently shared a study that predicted a 647 percent increase in data science jobs by 2020. It is an exciting industry that combines the wizardry of machine learning, statistics, advanced analysis and programming, bringing to light data often buried deep in the trenches.

But this emerging field can be a bit confusing. What does a data scientist actually do? We reached out to five Austin tech companies to learn what their data scientists are up to, why they love their roles and what advice they have for those interested in joining the field.


HomeAway, part of the Expedia, Inc. family of brands, offers more than 2 million places to stay in 190 countries. The Austin tech company has a small team of data scientists they recruited through Boolean searches on Google, local meetup groups, data science conferences and LinkedIn searches. We caught up with Brent Schneeman, senior director of global data science, and HomeAway’s head economist Justin Rao to learn more about how they found their way to the company.

Why did you enter into data science?

Schneeman: It seemed like a natural evolution in my career. Having been a software dev, I had “data acquisition” and “build things” skills, which are required. My mathematics background provided an analytic grounding and my photography hobby provided a visual aesthetic for storytelling. HomeAway at the time was transitioning to a transactional business model and I felt that a “'build it” approach to data science and machine learning would be a good muscle for the company.

Rao: Why I did get an economics Ph.D? I like math and I wanted to study and model how people made choices.

How did you break into the field?

Schneeman: I was an engineering lead at the time of the business model pivot. That meant that I wasn't writing code anymore. It also meant that I was expected to pay greater attention to “the business. That gave me access to data that was begging for advanced analytics. I side-of-desk implemented a few data science projects (learning the trade as I went) and then approached my VP with the proposition: "HomeAway needs a muscle here. Let me build it." I was moved from a director to an individual contributor with the mandate to build out that muscle. And I haven't looked back.

Rao: I didn’t enter “data science” like many who end up in data science. I did a technical post-graduate degree and found my way to it.

What do you enjoy most about your work?

Schneeman: I enjoy building the science culture — encouraging the members of my team to demonstrate what machine learning and data science can bring to product and business. I also enjoy the time spent on various side projects.

Rao: Solving hard problems with great colleagues. Working with people who have totally different skills than me (data science is a very broad category).

What advice do you have for people who are interested in a career in data science?

Schneeman: Learn how to access data. SQL is the lingua franca of data access, so that is required, but familiarity with various APIs (at a code-level) is useful. Don't get hung up on the beauty of the data and the science — the efforts have to support the common purpose of whatever organization you are with (a business, academics, a Kaggle team, etc). Finally, quantitative skills are a must, but storytelling skills will allow you to convey the concepts to broader audiences.

Rao: Learn the core math machine learning is based on —  linear algebra, numerical optimization, differential equations. Take the toughest classes offered at your university. Participate in meetups, enter machine learning contests, etc.


With plans to expand its data engineering team from one to four by the end of the year, Duo currently relies on principal data scientist Brian Lindauer for developing new product features that improve security and usability through data and machine learning. Lindauer shared how he accidentally fell into the curious world of data science.

Why did you enter data science?

I love surprising/counterintuitive research results, and data science is great at turning up these kinds of findings. This is also a field that is changing incredibly rapidly, so there is always something new to learn.

How did you get into the field?

I sort of fell into data science by accident when I took a job as a software engineer at a network anomaly detection startup. I joined because I was interested in the security space, but over time, I started to find the machine learning aspects even more interesting.

What do you enjoy most about your work?

If I had to choose just one favorite part of working in data science, it would be that "aha" moment when you finally reason out a solution to a hard problem. But on a more day-to-day basis, it's hard to beat the learning that comes with working with the smart and diverse group of people we have at Duo.

What advice do you have for people who are interested in a career in data science?

Hands-on experience and theoretical knowledge are equally important. It’s definitely worth your effort to learn how things work, rather than depending solely on black box machine learning libraries. The black box won’t always work.


CreditCards.com has one data scientist out of a company-team of about 100. Her name is Lenae Stoner, and her core responsibilities include acquiring data, identifying and analyzing trends within complex data sets, assisting in interpreting and validating A/B test results across web properties, developing interactive dashboards and joining tactical planning discussions. Following her passion for math and statistics, Stoner said math career options open up for those who have an advanced statistical degree.

Why did you enter data science?

I like applying math to the messiness of the real world. Statistics is one of those fields that the more you study it, the more interesting it becomes.

How did you get into the field?

I got a second major in math in addition to chemistry when I was in school, because I liked math, but I didn’t really think too many jobs were available to people with math degrees. By the time I graduated, I realized there were a lot of opportunities available to those who have advanced degrees in statistics, which I ended up enjoying more than pure math. So I then got my masters in statistics.

What do you enjoy most about your work?

I really like solving problems and trying to determine what story data is telling me.

What advice do you have for people who are interested in a career in data science?

I see a lot of data science Massive Open Online Courses (MOOCs) that mostly just tell you how to run some code but don’t get into the details. As you are learning about new algorithms and statistical methods, make sure you understand how they work and how to tell when they aren’t really working with your data.


Civitas Learning’s platform is fueled by institution and student-based data that's used to increase student success and graduation rates. The team consists of nine data scientists, led by their Chief Data Scientist David Kil, and works on a variety of different projects. Their work leads to predictive and scalable models while collaborating with engineers, data engineers and the product team.

We connected with John Daly, a member of Civitas’s data science team, who began using machine learning algorithms in grad school. 

Why did you enter data science?

Many data science jobs have mathematical, software engineering and business intelligence components to them. As someone who likes multifaceted problems, this had a strong appeal for me. I had also built up skills in engineering and research in my career, so it was a natural fit.

How did you get into the field?

I spent a lot of time in grad school using machine learning algorithms to tackle scientific problems. By the end of it, I knew I wanted to work in industry, but I wasn't sure where. I started looking around and eventually found someone willing to give me a chance analyzing econometric data, despite coming from a computational neuroscience background. I also took a few online courses to round out my skills.

What do you enjoy most about your work?

Developing and testing hypotheses for real world problem. If your hypothesis is right, you might get to solve a problem and deliver real value to someone. In my current role, I build models to identify at-risk students, so performance increases in models mean helping people at a critical juncture in their lives. If your hypothesis is wrong, you might actually learn something you didn't already know. That is its own reward.

What advice do you have for people who are interested in a career in data science?

The two pieces of advice I typically give to aspiring data scientists are to learn their field and to find a good mentor.

I've seen too many data scientists who stopped at learning machine learning techniques and software libraries. Knowing which parameters in an estimator to tweak might lead to small performance improvements. Knowing that the data you actually needed to collect or that you need to reframe the problem can lead to much larger performance gains. Identifying a good mentor can be challenging. I look for the ability to communicate clearly, especially with non-technical audiences. Anyone can snow people with jargon, but explaining complex concepts in simple language or analogies is a hard.


Gopal Krishnan, CognitiveScale’s VP of engineering and professional services, said they’ve found their data scientists through affiliations with the University of Texas Austin, other institutions and referrals. Along with some engineers, their team of 16 data scientists works on engaging with clients to understand pain points and data characteristics, analyzing data and developing models to unveil insights that are integrated into CognitiveScale solutions.

Natalia Arzeno, Nikita Namjoshi and Michael Dobson of CongitiveScale’s machine learning engineering and research teams opened up about their career path and what they love about it.

Why did you enter data science?

Arzeno: I had research experience in signal processing of clinical data but wanted to research a broader set of problems, including more complex predictive analytics problems, for which machine learning is essential.

Namjoshi: When I took my first probability theory class in college, I became very aware of how statistical claims play a massive part in so many aspects of life. Sometimes these claims are founded in solid mathematics, but often they aren’t. People feel bold when they make claims backed by data, because data is objective. However, the people interpreting data are usually not objective. Furthermore, as a representation of our world, data is often embedded with human biases and perspectives.

I entered data science because I wanted to learn for myself how to evaluate statements made from data. I want to thoroughly understand the rigorous statistical methods that unlock information within data, and to push back against irresponsible data science when I see it. 

What do you enjoy most about your work?

Arzeno: Researching approaches to solving the problem at hand given both the project objectives and the subtleties of the data.

Namjoshi: The people! Every day I get to collaborate with amazing data scientists who are committed to mentorship and delivering high-quality work. I am constantly learning and applying new techniques. I never have a boring day.

Dobson: In machine learning, there are many different ways to approach a problem and a variety of modeling techniques one can apply. Each problem will have its own characteristics which make some of these techniques better suited than others. This is why I entered the field. I enjoy thinking through many of these different approaches and seeing how each one tries to model the situation from a different angle. One of the things I enjoy most about my job is staying on top of new research and developments and then discussing their applications to our company's problems with my teammates.

What advice do you have for people who are interested in a career in data science?

Namjoshi: Learn to be fearless when it comes to math. I know so many brilliant people who are terrified of math. Somehow we get it into our brain’s when we’re young that math is this terrifying subject that is to be avoided at all costs. Over the years, I’ve learned to not be daunted by math. Sure there are off-the-shelf packages you can use that allow you to run countless powerful algorithms without really knowing what is going on behind the scenes. But if you really want to be an effective data scientist, you must understand the theory and never shy away from the math. 

Dobson: Data science and machine learning is very broad and deep. For someone trying to enter into the field, I'd recommend finding an experienced mentor who can provide guidance. The number of questions can seem overwhelming ranging from which modeling techniques are best for which types of problems to what are the differences among many of the software tools. An experienced data scientist who can steer someone in the right direction can be a great help.


Images provided by companies and social media. 

Want to get in touch? Let us know with a tip or on Twitter @BuiltInAustin

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