Overcoming Imposter Syndrome | Paul McLachlan, PhD on The Artists of Data Science Podcast
On this episode of The Artists of Data Science, we get a chance to hear from Paul McLachlan, a data scientist who has over a decade of experience applying his knowledge and expertise to academia, corporate businesses, and entrepreneurial endeavours. His contributions and expertise have led to numerous startups and nonprofits inviting him to serve as an advisor. He gives insight into how what sparked his interest into the data science field, his tips for beginners in data science, and how he stays motivated.
Paul shares with us his powerful journey from being a high school dropout to getting his PhD in computational social science and becoming the A.I. research leader for the Consumer and Industry Lab at Ericsson Research. This episode is packed with advice, wisdom, and tips that will change your mindset. It was a great honor interviewing Paul!
Some notable segments from the show
[3:57] How Paul became interested in data science
[6:19] How Paul got over his fear of “looking stupid”
[27:42] Actionable tips for cultivating the habit of critical thinking
[40:07] Advice on how to be the hero when you feel like a failure
Where to listen to the episode
Paul’s journey into data science
Paul was a high school dropout, and one of the few people who has a GED and PhD. What really sparked Paul’s interest in data science was a math class he took during his undergraduate at Columbia. He put off taking this class until his last semester, since he was afraid of making mistakes and getting a poor grade. The teaching assistant in the class helped Paul, meeting with him during office hours and helping Paul gain a deeper understanding of math. This new found foundation excited Paul to delve deeper into statistics.
Once he gained the foundation to think like a data scientist, he was excited to apply his math skills to answer questions. This fuelled him through graduate school and now in his career.
[3:57] “So I’m a really curious person. And I thought, oh my God, there’s this technology or this technique that you can use to test things, to understand questions. That is just a coolest thing I’ve ever heard of. And that fuelled me through graduate school and now in my career, because I just think of this as a technology to answer questions in a rigorous way. And I just think that’s the coolest thing and that’s what motivates me.”
Where is the field headed in 2–5 years?
What Paul is really excited about is 5G. The connection between 5G and data science might not be very obvious, but the biggest impact will be that 5G reduces latency. This means the lag time for data to be recorded and processed will be significantly reduced.
This will improve data precision and accuracy.
[8:20] “And also is really interesting implications for privacy. But for me, I think the real shift in the near-term is going to be towards thinking of real time data and the type of systems we can build to work with real time data rather than trying to build systems to work more and more with larger a larger historical data sets.”
What will separate great data scientists from the rest of them?
Paul considers the willingness to ask good and difficult questions as the differentiator between the good and the great data scientists. One of the challenges in data science is that data scientists need to have an incredible amount of domain expertise, which requires data scientists to keep on top of the literature, while also being subject matter experts in specific industries.
A great data scientist will be able to bridge the gap between these two facets, and be able to communicate and bring value to their specific industry.
[11:28] “It also means that we have a lot of work to bring our non-technical stakeholders along the journey with us because we can build the best and most innovative cutting edge algorithm. But if our sales team doesn’t feel empowered to talk about it, that can be a challenge. Or if our stakeholders don’t understand the research and the innovation that went into it. That can also be a problem. So I really think a great Data scientists brings domain expertise and machine learning, subject matter expertise in their industry and an ability to bridge the two.”
Key takeaways from the episode
How A.I. can help fight COVID-19
[21:37] I think we can find ways to minimize societal cost using A.I. and data science. There is also the question of being able to develop a vaccine at scale. This means supply chain optimization and manufacturing optimization, all which require data science. The number of ways that data scientists can get involved is limitless, but we need to make sure that the work is embedded in a real stakeholder need.
Extended Reality and Virtual Reality
[27:15] XR (extended reality) and VR (virtual reality) are both areas where research is being conducted to ensure security. For example, making sure the content you see is safe, or that deep fakes are not a major concern. Ethics with this technology is on the forefront of research and development, and it has a lot of implications for the future of how people interact with one another.
Tips for beginners
[32:11] You need to be proactive in your communication with non-technical stakeholders. You need to ensure that you can communicate the importance of your work, and the value that it brings to the organization. You need to be able to explain how your tools work, and what they do for the stakeholders. This requires a lot of experience and practice, but is super critical.
Important soft skills
[35:29] Be humble, be curious. Talk to people who you might not have interacted with before. Ask questions, even if they might sound very basic. Read books, nonfiction and fiction. All of this is a great foundation to build on.
[44:22] Try to make time in your week to have fun. This can mean different things for different people. For example, I like to learn about other domains that I don’t know much about. This fuels my creativity. Remember, your career is a marathon, not a sprint. You must find ways to have fun to stay motivated and have longevity in your career.
[19:05] “Data science is really a collective endeavour… even the most skilled and successful data scientist is going to have to be able to successfully work with technical stakeholders, non-technical stakeholders…”
[34:51] “…Start from a position of humility…that that can go much further for data scientists than always trying to be the smartest technical person in a conversation…”
[45:29] “Having fun and staying connected and staying entertained is actually part of your job responsibilities rather than something that can be set aside.”
The one thing that Paul wants you to learn from his story
[47:13] You don’t know the story of the person who is sitting across from you or sitting next to you. We assume that everyone has had such a straight and linear path of success without any setbacks. That’s just not true. Everyone has setbacks. It is critical to keep in mind that everyone you’re interacting with, from your CEO, to your classmate to your professor, is a human being.
From the lightning round
Data science superpower
Speak more slowly.
What motivates you?
Advice to 20 year old self
Careers are marathons, not sprints.
Topic outside of data science we should study
Books and other media mentioned in this episode
Where to find Paul Online
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The transcript for this episode can be found here.