Louvere Walker-Hannon, Mathworks
Louvere Walker-Hannon, Application Engineering Team Leader at MathWorks, teacher and STEM Ambassador, talks about acquiring skills and overcoming barriers to work in data science.
Louvere Walker-Hannon has worked at MathWorks (the company that makes MATLAB) for over 21 years, where she’s also a STEM Ambassador. She studied biomedical engineering as an undergraduate at Boston University and did graduate work at Northeastern University in geographic information technology with a specialization in remote sensing. She loved working with MATLAB as an undergraduate and when MathWorks came to the career fair when she graduated, she sought them out, got an interview, and has been working there ever since.
She says there are both technical and non-technical skills that are valuable in the field of data science. Technical skills include coding, some programming, a foundation in mathematics, some statistics, and in some cases physics. Non-technical skills are also very important. It’s critical to be able to communicate your findings clearly using a variety of techniques. She says stay away from technical jargon and communicate as if you’re having a conversation. A second important skill is active listening, to be open to suggestions from others, especially those who are new to the field.
She explains that there are also barriers to people entering the field of data science. For some people, coding is a barrier to engagement with data science as many people in STEM professions are not comfortable with coding. MathWorks is doing more development to provide user interfaces or apps to give people a starting point without having to rely on writing code.
There are also concerns about model interpretability where it’s difficult to get insights into how certain models work. More people are gaining awareness about the topic, and that's leading them to explore how to implement it and ask why it’s important. She is noticing that more people are trying to incorporate model interpretability into their data science applications.
One of the systemic barriers is implicit bias. People are used to working with and being around people with certain characteristics. And in a work setting, there could be a project coming up, and there are several individuals who could work on this project. Many times, the people selected to work on a project tend to be the same individuals. But then it begs the question, when do others get the chance?
There’s still a lot of under-representation from various population groups in data science. Even if people from an under-represented group have the skills and education, if they don’t feel like they belong, that can impact their productivity. It’s important to build a sense of community and have someone guide the person, make them feel welcome and help them become a part of the culture, so they can understand what they can do in order to thrive.
Louvere is also a STEM ambassador at MathWorks where she volunteers in STEM advocacy and outreach in schools or on STEM panels. She loves hearing high school age and younger students at science fairs talk about their projects and see how proud they are of their work. This gives her hope that young people are excited about data, about analysis, and communicating their insights to others.
Connect with Louvere Walker-Hannon on LinkedIn and Twitter
Find out more about Mathworks
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher
Subscribe to the WiDS Podcast: