Welcome to episode #093 of the SDS Podcast. Here we go!
Today's guest is Data Scientist at Liquid Biosciences, Beau Walker
Through studies in law and ecology, Beau Walker found his calling as a data scientist. Tune in today to hear about his journey, as well as his tips on leveraging LinkedIn for a successful career.
You will learn about evolutionary-based machine learning and how it compares to other machine learning methods, and hear us discuss Beau's patents, as well as when a trade secret may be a better strategy than filing a patent.
Let's get started!
In this episode you will learn:
- A Remarkable Journey Away From and Back Into Science (4:50)
- Transitioning from Law to Data Science: Transferable Skills (18:08)
- Evolutionary-Based Machine Learning in a Nutshell (25:29)
- Advantages of Evolutionary-Based Machine Learning (28:41)
- Patents and Trade Secrets in the US (34:31)
- The Value of Being Active on LinkedIn (40:16)
- One Key Point for Data Scientists to Future-Proof Their Careers (47:16)
Items mentioned in this podcast:
- The Visual Display of Quantitative Information by Edward Tufte
Kirill: This is episode number 93 with Data Scientist at Liquid Biosciences, Beau Walker.
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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.
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Hello everybody, and welcome back to the SuperDataScience podcast. Today we've got something very special prepared for you. Today on the show we had Beau Walker. And we just finished our episode, and now I'm super enthusiastic about everything! The amount of things that he shared is crazy. And very interesting things at the same time. So Beau's got a crazy background, and we'll talk about that just in a bit. It feels like he's had like five different careers. And what he does now for a job is a specific type of data science, which is evolutionary programming based machine learning. And the description he gave is intense. It's like when they create this environment for algorithms to evolve on their own, or models to evolve on their own, just like we had in evolution, when animals evolved. So how they reproduce, how they fight with each other, survival of the fittest, and things like that.
So it's a very, very interesting space. I had no idea. I had some interaction with people in a similar space, but I had no idea it was so evolved, and exactly what it's all about. So this was going to be very exciting for you to check out. Also, we talked about patents and trade secrets. So Beau, apart from being a data scientist, he also has a degree in law, and specifically in the area of patents and trade secrets, so that can be very useful.
And of course, we talked about his journey, how he went through all these different careers, what he experienced, what he felt, and what choices he made down his career path. So a very exciting episode ahead. Can't wait for you to check it out. And without further ado, I bring to you Beau Walker.
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Welcome everybody to the SuperDataScience podcast. I've got a very exciting, and very interesting guest today, Beau Walker. Beau, welcome to the show. How are you going?
Beau: Thank you Kirill. I'm doing great. Excited to be here.
Kirill: Where are you calling from?
Beau: I'm calling from Orange County, California.
Kirill: That's so cool. How's the weather in Orange County?
Beau: It's beautiful, 75 degrees, sunny.
Kirill: What's 75 degrees in Celsius?
Beau: Oh, let's see, now you're making me think on the spot. Let's see, twenty-something? Yeah. I don't know!
Kirill: 29? 25?
Beau: I lived out of the US, and dealt with –
Kirill: There we go. 23.8, right?
Beau: Yeah, there we go.
Kirill: Ok, well nice, nice. You guys are slowly headed into winter, so it should be pretty full.
Kirill: So Beau, we got in touch through a common connection, through our dear friend, right? Who's our friend?
Beau: Yes, Ben Taylor.
Kirill: Yeah. So how do you know Ben?
Beau: Ben and I are from the same place. I'm from the state of Utah, in the US, originally. Ben lives there now. We've been connected on LinkedIn for a number of years, exchanged back and forth. And his current co-founder, we actually graduated from the same programme at BYU, the university, but not at the same time. So we just have a lot of common connections.
Kirill: It's interesting how the world works, right? I also know Ben Taylor from LinkedIn, also for a number of years, and none of us, neither you nor I, have met him in person, and yet with him, we've already talked about so many things, and now he's connected us, and it's for everybody listening out there, it's such a powerful world these days, that especially through LinkedIn and online, you can create some amazing connections and friendships. It's really cool.
Beau: Absolutely true.
Kirill: Awesome. So, Beau, you've got like a crazy story. You've done consulting, you've done marketing, you've done law, you've done data science, you've done biological things. I don't even know where to get started. I'll probably pass it over to you.
Beau: In the past, when recruiters have looked at my resume, they've been confused.
Kirill: To say the least, yeah? At the least, confused.
Beau: Yeah. But there is a common story, and I have a lot of varied interests, but there is a common thread. And I feel like, at least personally, that my background lends itself very well to data science. Data science is still a young profession, at least by that name, and it's often a profession where the rubber meets the road between data and actually making it translate into something useful for business. And so, a varied background, a different background, can be very useful.
So my background is that my dad's a marketing guy. He's been in marketing his whole career. He's also an inventor. He has a number of patents. And just one of those guys, entrepreneur, inventor, always talking about ways to make things better, or new inventions, and that really stuck with me. And when I went into university, I've always had a really strong interest in technology and science, and I went into biology. And pretty early in my undergrad, I got involved with some great labs doing research with some great professors.
But at the same time, working to pay my way through college, I was doing marketing on the side. I went and got a Masters degree with the same professor that I had worked with in my undergrad in ecology, and that's really where, doing research with him, I first got exposed to doing science, the scientific method, gathering and analysing and presenting data, which is really the core of data science. My Masters thesis and Masters work required a ton of programming. I was in an ecology and evolution programme, and so a lot of those skills I had to pick up myself.
Kirill: Yeah, I can imagine. That's some tough stuff.
Beau: Yeah, so I was looking at erosion in the desert, the US Southwest desert, and I was developing photogrammetry methods to use images from small digital cameras, local images, and calculate the amount of erosion that was occurring at a small scale.
Kirill: Sorry, erosion is when the desert takes over green areas? Is that what it is?
Beau: So that's when wind or water moves soil or sediment away. That's erosion.
Kirill: Ok. So it becomes more desert?
Beau: Yeah. In the US Southwest, it's a huge problem because one, you get these huge dust storms that blow through and lose visibility and cause car accidents and things like that. And then from an ecology standpoint, there's all these nutrients for plants and stuff that get blown away. The dust from these deserts actually lands on the Rocky Mountains and it causes the snow to melt up to 30-40 days earlier than it usually would. So, there’s all kinds of change in the ecosystem because of this sort of thing, and people make the dust problem worse by riding ATVs or grazing animals.
That was kind of the context of what the research was, but the part that I really liked is I was developing these new methods that no one had used before, you know, taking images and creating 3D models from them, and then over different time periods calculating the difference in volume that had been lost along with a bunch of other data we were collecting. That required a lot of programming in MatLab, in R, and in Python. And I think that’s where I really started to develop my data science skills, when I was working with large biological datasets, was generating a lot of data.
And about that time, I got a job at a marketing consulting firm as their data scientist. That’s when I first started taking these scientific skills that I had learned and employing them in marketing and advertising. And instead of analysing dust, which my wife assures me is a very boring subject, I was analysing social media data, web analytics and sales data and stuff like that, helping develop predictive models and really analyse the effectiveness of different campaigns. That’s how I made the transition from my Master’s into data science.
Kirill: And just before we continue, how did that feel? Which one did you prefer more? How did they compare, you know, using science in science or using science in business?
Beau: You know what? They both are really exciting to me. My plan early on was to go get a PhD. I didn’t end up doing that, and I think the reason was I love science, but especially in ecology, it’s hard to feel like what you do has an immediate impact. You know, in the U.S. there’s all kinds of legislation and other stuff like that. It can take a really long time and people to even listen to your research, for a change to actually happen. And I feel like on the business side—you know, sometimes the results are immediate. So I think that, and having grown up in an entrepreneurial marketing context, I was drawn to that. But for me, I felt like I was doing science in both cases, you know. One purpose of science is to uncover and understand the underlying laws of why things are the way they are. In marketing and business, there are laws.
Kirill: Yeah, I totally understand. I can completely relate to that concept of immediate results. It’s very rewarding to see your work actually bring some sort of change very quickly.
Kirill: Okay. And then what happened after that? I’ve got a feeling like we’re just getting started here on this. And just a quick note for those listening, Beau gets asked all these questions so many times that he even wrote an article “There And Back Again: My Return to Science.” So you’re kind of walking us through this article, right? Through the main points, but in more detail?
Beau: Yeah, there’s a nice “Hobbit” reference for those Tolkien fans. So, it was during the recession in the U.S. and I had been planning after my Master’s degree, originally going to get a PhD, but then maybe feeling I didn’t want to go into academia but, you know, I knew that I’d loved inventing things and loved business, so I started thinking about a career that—now I feel kind of stupid that I didn’t immediately grab onto data science, but I started thinking about a career where I could combine my love of science and my love of business. And looking back to my dad having got a bunch of patents, I decided, talking with a bunch of patent attorneys, that I wanted to go to law school specifically to become a patent attorney.
Beau: So I went and I got the wrong doctorate for data science, a more professional degree – a JD or Juris Doctor. (Laughs)
Kirill: And you got it very quickly. It took you like 3 or 4 years, right?
Beau: Yeah. In the U.S. it’s a professional degree program and it only takes 3 years.
Kirill: That’s really impressive. So you have a Master’s in biology and a doctorate in law.
Beau: Well, yeah. I think PhDs in the U.S. wouldn’t consider JD a doctorate, but yeah, it does have ‘doctor’ in the name. But I’m not Dr. Walker.
Kirill: (Laughs) Okay. And where did that take you?
Beau: So, fairly soon into law school, I started to realize how much I missed science and how much I missed programming.
Kirill: Because you got none of that in the law school, right?
Beau: No. And, in fact, the way that law works is truth is all relative. In science it’s a lot more, you know, you have the data to support your conclusion or not, and the law is you can convince the judge or not. Or the jury. So that was always kind of uncomfortable for me. So I started to take on some freelance clients, doing stuff on the side while I was in law school. I hope my contracts professor isn’t listening to me, but I was the kid that he’d look over and I was always programming on my laptop instead of taking notes. (Laughs)
Kirill: And how did you find the clients, just out of curiosity, was it some website online or somehow?
Beau: A combination of personal connections that I knew from the work that I’d done before. Sometimes I’d get clients off of freelance sites like Fiverr or Upwork.
Kirill: Yeah, I always recommend Upwork to people. It’s a very good website for that sort of stuff.
Beau: Yeah. And then I had a couple of my own projects that I was working on. A couple of months into law school, I started working for a law firm, intellectual property law firm, and got a ton of experience drafting patents, litigations, doing trademarks, all of that. I worked mostly full-time, 20 to 40 hours a week all throughout law school there. And it was a great experience because the firm where I was at had really great training, and I got to do all the things that I would do as an attorney and really good experience. You know, I drafted a ton of patents in biotech a lot and software and data science areas, but frequently we’d have inventors come in and I was jealous that they were doing all these cool things and I was just writing about it. So that was just always in the back of my mind, like, “They’d invented this really cool thing.” And sometimes patent attorneys play a small part in helping make it a little bit better, but I’m jealous that I’m just writing about the stuff that they’re doing.
Kirill: But jealous in a good way, like it pushed you to change, pushed you to realize things about yourself?
Beau: Yeah. I actually was contemplating, like, “Can I jump back into data science full time?” I kind of tested the waters a little bit. And it was hard while I was in law school, I was committed to finish that out. But almost a year after I graduated law school, I had my current boss reach out to me. He found me on LinkedIn just out of the blue, you know—and we can talk about LinkedIn, but that’s maybe a whole other podcast. (Laughs) I’m a big fan of LinkedIn. He said they were looking for a data scientist and he really liked my background. And I got to talking to him and I was really fascinated. He had bioanalytics company, clients were like pharmaceutical companies and health care, and they had their own form of machine learning that was evolutionary programming-based. My Master’s degree is in evolution, so that was really interesting to me.
So I decided to leave law and to join that company. That kind of brings me to where I am now. I’ve done a lot of freelancing for various companies of different sizes, everything from marketing to sensor companies, and now I’m the data scientist for a biotech/bioanalytics company, so I’ve kind of gone full circle.
Kirill: That’s really cool – a bioanalytics data scientist. And it’s an amazing story how you went there and back. You say that on one hand you really miss data science, and I think that it was probably a necessary step in your career to go away from data science. We realize how much we miss things only when they’re absent, like the saying, “Absence makes the heart grow fonder.” And at the same time, I’m sure there’s a couple of things that you probably picked up in this law degree that you were doing that you now use in your career. Could you mention something? For somebody who’s a lawyer, or studying law, out there and listening to this podcast, what is some skill or habit or something that you picked up during law that you’re still applying in data science?
Beau: Oh, absolutely. There’s a couple of things. One, lawyers are trained to be very good at seeing and even being able to argue different sides of the same issue. And, you know, a lot of times when we’re analysing something in data science, it’s not entirely clear immediately what the data are saying. And sometimes you have to be open to “Maybe the algorithm or method that I’m using is telling a misleading story and I need to look at it from another angle.” So that’s one thing law taught me.
The other thing, I think a huge part of a data scientist’s job in many companies is kind of being the gap between what the data are saying and what that actually means in terms of what the business should do. And those communication skills are something that being in the legal profession definitely helped me with in terms of being able to communicate complex subjects. So that’s another thing.
And the third thing, the area of patent law is really interesting, especially if you’re drafting patents, because like data science, it’s a profession where you’re always learning. You’ll have an inventor come in and he may have invented a new way of designing or using an oil rig. So then you have to do a bunch of research on all the nuances of how oil rigs work. And then your next client will have invented a new database schema or something, so you have to become enough of an expert, or if not an expert, conversant enough that you could draft a patent in it. You know, just the ability to quickly come up to speed on whatever the topic is immensely useful in data science because the field is always changing, always encountering new problems that maybe aren’t exactly like what you’ve encountered before. So the ability to know where to turn to find the information you need is really important.
Kirill: Yeah, definitely. It’s a skill that you can’t just learn overnight. It’s something that you have to practice, practice and practice. Those are some solid skills that you took away from your law degree, and I’m sure a lot of people will find this useful. Okay, now you are passionate about inventing, right? You have, what, 20, 30-odd patents and trade secrets? Or is it more than that?
Beau: Yeah, I’ve always kind of had a bunch of ideas. And what’s really exciting about my current role is that the company I’m at places a huge value on intellectual property. So I kind of have a dual role. I’m there as a data scientist primarily, but heavily involved with our IP strategy as someone who is familiar with that world and help manage our outside counsel. Since I’ve been there, we’ve started filing a ton of patents, I’ve invented a number of things. It’s just been really fun. You know, everything from new ways of analysing biological samples to new unsupervised learning clustering methods and stuff like that. It’s just been really fun to have that creative side.
Kirill: That’s really cool. And it sounds like they hired you as a data scientist, but now you’re doing not just data science, but also the patent side of things and helping out with that. Can you tell us a bit about that, because that sounds like a very interesting career move or career development stage where you came into the company to do one thing—and correct me if I’m wrong, maybe they hired you right away to do both things. Can you tell us about how your role has developed in these past seven months that you’ve been there?
Beau: Yeah. We have the benefit that the company is smaller. You know, if you’re in a larger company, you maybe don’t have that flexibility. But one thing that was really attractive to me about this role is that there is a lot of opportunity for me to help shape the company. They had invented a number of things before I joined the company and they’d filed, I think one patent. And when I joined the company I said, “Hey, you know, I’ve just spent the last three years around patent attorneys. We should be approaching this differently. You have a ton of value here and you bring incredible value to the company. Let’s start filing some of these things.” So we filed a bunch of patents since then, both on the old stuff and new stuff that we’ve come up with. It’s been me not being afraid to say, “Hey, I have experience in this and this would be useful,” and then having the data to back up and say, “This is why it would be useful,” it’s kind of the same skills of advocacy that are useful.
Kirill: Awesome. And that sounds like a great example to everybody listening that there is room always to leverage your existing skills. You’re going in as a data scientist, but you have interest in something else, you should express that interest to your manager, to your boss, to other people and proactively work towards making that happen, making your role shift in that direction or expand in that direction.
And because you’re so passionate about it, you’re inevitably going to be happy doing it and you’re going to be bringing even more value to the company so people are always going to be open or should at least always be open. And that’s for the managers out there, to be open to suggestions like that because it’s a win-win for everybody. That’s a great example of that. Awesome!
Beau: Yeah, I think that’s good advice.
Kirill: Yeah. And thanks for sharing that in your story. You mentioned a couple of tools that you used in your degree – MatLab, R, Python. What are you using predominantly now?
Beau: In my day-to-day, I primarily use R just because the data manipulation and visualization tools are really great. The bulk of our machine learning is done with our proprietary software that we’ve coded that’s in combination with some other languages. I do the bulk of my day-to-day in R with some Python.
Kirill: Yeah, gotcha. And which one do you prefer, R or Python?
Beau: Probably R because I’m more familiar with it. I’ve use it a lot more. But Python is great and it’s quickly in my mind gaining all the benefits that R has.
Kirill: Gotcha. This evolutionary programming-based machine learning is very interesting. I think we’ve had a guest before, Deblina Bhattacharjee, she was in evolutionary-based artificial intelligence. Without revealing any trade secrets or patents, can you give us just a general overview of this topic area? What is evolutionary programming-based machine learning?
Beau: Yeah. I can give you a very general use case of how it works with what we do.
Kirill: Yeah. That would be great.
Beau: There is this idea, a term called ‘inverse problems.’ An inverse problem is one where you don’t necessarily know what the problem is. You maybe just have data about it. You may have a set of predictors or data about circumstances and data about outcomes. And there’s some mathematical or statistical model in the middle of those that relate the predictor the outcomes. That’s kind of generally the goal of machine learning.
But specifically, in biology and in human health, there are mathematical rules that govern disease and other things like that, but we don’t necessarily know beforehand what those relationships are. One way that we approach that problem is through evolutionary programming. And the idea, and the way that our software works, is that we start off randomly generating millions of models, mathematical models or algorithms that comprise math operators like addition, subtraction, division, sine, cosine, constants and then n variables, any of the variables in the dataset. We put them in the digital ecosystem and then let them evolve.
At the beginning, the algorithms are very bad, they predict the outcome very poorly. But you’ll have some that maybe instead of getting a coin flip 50/50 chance of predicting the outcome, there will be 51%. So that algorithm will kill off other algorithms and then they’re allowed to either mutate, duplicate themselves or mate with other winning algorithms.
Kirill: (Laughs) This is so cool.
Beau: Yeah. So you go through multiple generations, and there’s a lot more specifics of how to get this to work, but what we end up with is a predictive model that’s evolved to the dataset to predict the outcome of the problem. There’s a couple of questions that we always get. One is, “What about overfitting?”
Kirill: Yeah. You were reading my mind. I was just sitting here thinking that.
Beau: Yeah. That is a concern for any method, but evolutions can be really good about overfitting. And the bulk of our IP, or if not the bulk, a good portion of our IP deals with this issue, so there’s a couple of ways that we deal with that. One is to make sure that we have training validation and test sets. There’s a number of ways to deal with that, but what we end up with is a model that is small, typically models that we produce out of our process are between 5 to 15 steps and they have maybe anywhere from 3 to 9 variables and a couple of different math. And they’re very robust. They hold up really well in terms of sensitivity specificity or area under the curve or whatever across different out-of-sample datasets.
The reason that this is a really powerful approach in health care is that often in medicine, it’s not just enough to predict an outcome. You need to know why and you need to know the underlying mechanism. Using this approach, we can take datasets that have millions of variables per patient and bring that down to the 3 to 9 most important biomarkers or whatever. It’s really powerful.
The other benefit is that a lot of deep learning/machine learning techniques are very data-hungry, but in health care and pharma, you often have datasets where you have an N of 60. You may have a couple million biomarker variables per patient, but it’s only 60 patients deep. Evolution can deal with that, though. That’s kind of the general principle of what we do as work. There’s a lot more detail in it, but as someone who came – at least academically – from an evolutionary background, I’ve always been intrigued by evolutionary programming methods, and they were initially very popular when they first came out, kind of like neural nets were, but ran into a number of problems in terms of implementation, you know, the hardware wasn’t ready, it was very computationally intensive, and there were a number of issues with implementation. What we say at our company is, deep learning neural nets went through this where the hardware finally caught up and there were a number of key innovations in terms of implementation and that’s why they’re performing so well today. I feel like the same thing is happening for genetic programming/evolutionary programming.
Kirill: That’s fantastic. Thank you for such a good overview. And I really liked what you mentioned towards the end, that it’s important in medicine often to know what is the reason for certain outcomes and that your algorithms are therefore interpretable, that you can figure a way that you can get those variables out of it.
Beau: Human-readable, yeah.
Kirill: Yeah, and also that your algorithms in some ways beat deep learning, especially in the sense that deep learning is data-hungry, right? (Laughs) This is probably something that you talk to Ben Taylor about sometimes.
Beau: Yeah. (Laughs) Actually on LinkedIn I tag him in some posts, too. Deep learning is really great at some very specific things, but there’s a lot of use cases where, just like with any method, it’s not the right tool.
Kirill: It’s good that this alternative exists, right?
Kirill: I’m especially very happy to share this with our listeners because sometimes all you hear is deep learning – especially if you talk to pioneers in the deep learning field, you just get that deep learning can beat everything. But what if you don’t have enough data? What if you have, like you say, 60 data points? Well, apparently there are other ways, such as evolutionary computation or evolutionary programming, machine learning and AI which is really, really cool.
Beau: Yeah. I mean, it’s tempting to look at data science as a profession and feel like every situation where you use data is like Google or Facebook. That hasn’t been my experience. You know, I think they’re the most prominent examples, but they’re sitting on amounts of data that most industries can’t even dream of having. Their problems are different. Data science is just as important in those other industries, even with less or different types of data.
Kirill: I totally agree. It’s so exciting. What you’ve created seems like a model of the real world, but probably on steroids, meaning it evolves really quickly. But algorithms killing each other, mating with each other and duplicating themselves? That’s crazy. I can’t imagine how much fun you’re having at work.
Beau: It’s really cool. And it’s cool to use my ecology and evolution background because it’s been incredibly useful. And I’ve kind of put that on the backburner in data science over the past couple of years and coming back I’m like, “Nature has been doing things for billions of years for a reason: It works.” (Laughs)
Kirill: Yeah, I totally get it. And was it hard to recall all those skills from your biological background? You know, because you put it on the backburner for some time, was it hard to reinstate that?
Beau: No, because I’m really passionate about that type of stuff. And I think anyone who is connected with me on social media gets sick of the biology-related stuff that I post or “This cool article on evolution,” you know. They’re like, “There goes Beau again.” Most people don’t care about that. (Laughs)
Kirill: You kept up your passion, even while you were doing law and other stuff.
Kirill: Yeah, that’s important. Okay, that’s really cool. Thanks a lot again for sharing that. I wanted to go to your patent background. I think we’ve never had a guest who specializes in patents and trade secrets and it would be criminal of me not to ask you some questions about something there. First of all, what’s the difference between a patent and a trade secret?
Beau: In the U.S., because that’s what I’m familiar with—well, I have to also probably legally say I’m not an attorney. (Laughs) I just worked at an IP firm, I have a JD.
Kirill: This is not legal advice, everybody. Please consult your attorney.
Beau: Yeah. So, in the U.S., trade secret is something that—a really good example would be Coca-Cola’s recipe for Coke, something that they don’t want to get out public, but they hold secret in the company. And to qualify as a trade secret in the U.S., usually there’s a whole bunch of ways that you have to deal with that. For example, you have to make sure it’s really clear that it’s a trade secret, you have to have all the protocol in place to limiting information or who has access to it and stuff like that. A patent is kind of the opposite. The way that you protect yourself is by telling everyone about what you’re doing. Kind of the underlying purpose of patent law is the government says, “You tell us what you’re doing and what you’ve invented in the form of a patent, and if we grant it to you and decide it’s indeed something new that no one else has done before, we are going to publish it so everyone can see but we’re going to give you a monopoly for a certain amount of time where no one else can do it and you can actually enforce that right if someone copies you.
So, with a trade secret, the only way that you can enforce it is if someone has stolen it from you and then they go and use it. But if someone independently comes up with the same idea that you’re keeping as a trade secret, then you can’t enforce that because they didn’t steal it from you. They just came up with it.
Kirill: And moreover, they can go and patent it and then stop you from using your trade secret.
Beau: Yeah, potentially. So, that’s kind of the difference between both of those. And there’s business reasons for keeping some things as trade secrets. A lot of times, things that are kind of obvious but someone hasn’t thought of them yet, it might make sense to keep it as a trade secret. Or, you know, in the case of Coca-Cola, they don’t want their recipe public because they might be able to have a patent on it for—well, maybe not now because it’s gone on so long, but they might be able to have a patent on it for 20 years, and then any competitor could use their exact formula.
Kirill: Yeah, exactly. And that’s what you see when you go to a pharmacy and you’re asked do you want the—I don’t remember what the first word is, but they’re like, “Do you want this type of drug or do you want the generic drug?”
Beau: Brand or generic, yeah.
Kirill: Brand or generic. So, the brand is the guys who patented it, and then 20 years pass, and now everybody else is allowed to make the generic version, which uses exactly the same formula because they can use it according to the patent, it’s just a different company. So technically, it’s the same thing.
Beau: Yeah. And the thought is, especially for pharmas, that there’s a ton of risk in the R&D of creating drugs, so you want to incentivize people to take that risk by giving them that monopoly for a certain amount of time so that they could be the ones that benefit from it.
Kirill: Yeah. And that’s also why you sometimes hear about these drugs that cure certain diseases which are really hard to cure—I can’t give an example because I just don’t know this well enough—and one pill costs like $50,000. Even though it only costs the company $50 or $100 to create that pill, to put it together, they sell it for $50,000 because of the amount of time and money they put into research and development in the previous years. Now they have the patent for 20 years, so they have to get that money back. Otherwise nobody is ever going to be creating these drugs in the first place.
Beau: Yeah. That’s the idea.
Kirill: Okay. That’s really cool. So let’s say I’m a data scientist, I’m a freelancer and I’ve come up with this really cool new way of doing machine learning, something that I don’t think anybody has ever done before, and I want to patent it or I want to create a trade secret. Is there any chance that I can do that or do I have to be an organization?
Beau: You can definitely do it as an individual. I mentioned earlier in the podcast that my dad has a number of patents. They’re not in the data science space at all, but you just pay for it, you find a patent attorney or you can even file it yourself. That’s entirely possible. It’s extremely difficult – I would highly recommend someone hire a patent agent or an attorney just because I feel like governments intentionally make the process so complex. But yeah, you can go out and do it.
One thing I will say is, in the U.S. especially, it’s gotten a lot harder to patent anything related to software in the past couple of years. There was a case a couple of years ago called “Alice v. CLS Bank.” For a proper interpretation you should talk to a patent attorney, but it made it a lot harder to get patents in that space. Not impossible, you definitely can still do it. For example, if your invention improves the functioning of the computer, it makes it faster or something, those things can help. So I’d recommend, if you have something new that no one else is doing, talk to a patent attorney and figure out if you can protect it.
Kirill: Cool. Okay, so there are chances and people can somehow protect themselves?
Kirill: Okay, fantastic. Thanks a lot for sharing that on the patent and trade secret side. And I have one more question that we didn’t discuss, which sounds really interesting. Your boss found your LinkedIn and you said you had some good things to say about LinkedIn.
Beau: (Laughs) Yeah. So, this goes to the undercurrent of my whole career. We’ve just discussed my winding path. I’m a son of a marketer. You know, I’ve been doing digital marketing in some sense for the past 15 or more years.
Kirill: Yeah. You even have a website for that, right?
Beau: Yeah. I don’t know how many people I want to refer to that because I’ve had some issues with the host. Now, I might say it hasn’t been performing well, but yeah, LinkedIn—I’ve gotten so many opportunities from LinkedIn, and especially over the past couple of months. I decided to finally start practicing what I preach about LinkedIn, and be a lot better about posting and engagement. I post a couple of times a week and my average post gets about 50,000 views. They’re all usually on data science topics and hundreds of likes and comments and it’s been really cool. I post about things that are interesting to me, but mainly not sharing my opinion, mainly wanting to ask what others’ opinion is. And there have been some really great discussions on things that I posted, really smart people sharing their opinion. You know, I’ve gotten clients from LinkedIn that have just found me out of the blue. My boss was just searching for data scientists in Orange County and came across my profile because I’d done some things to optimize it so I showed up in search results. Yeah, I think it’s essential to pay attention to your digital footprint in this time.
Kirill: For sure. Would you recommend LinkedIn to people who are looking for jobs?
Beau: Absolutely. I think if you’re looking for a job, it’s absolutely important. I think even if you’re not looking for a job, you’re happy in your career, I think it’s incredibly valuable. One good example is there’s a data scientist at Facebook named Brandon Rohrer, you’ve probably heard of him. We actually went to the same undergrad, but he’s very active on LinkedIn even though he has no intention of ever leaving Facebook. He’s clear about that. So he’s not looking for a job, but he’s very active on LinkedIn, posts things that are incredibly useful. I think it’s really good to be involved in the global data science community even if you’re not actively looking for a new job.
Kirill: Yeah, for sure. You can make some great connections, as we said at the start, and you can just give back to people and share your progress. It’s a great community to be in. People do things for each other and they help and we grow together, so why not, right?
Beau: Yeah, absolutely.
Kirill: Okay, thanks a lot. I just have some rapid-fire questions to wrap this up. Are you ready for this?
Kirill: Okay. What’s the biggest challenge you’ve ever had as a data scientist?
Beau: In my current role, we have core services that we offer and then every once in a while we’ll have a specific request for a client. I mentioned that we’ve done some work in developing new unsupervised learning methods to solve a specific problem, and I think that’s been the most challenging thing that I’ve done. I will never pretend to be a mathematician. I’m a biologist and scientist first, you know. Math is an incredibly useful tool, but I invented this new algorithm, this new approach and it worked really well for this specific purpose. It was really challenging but very rewarding at the same time.
Kirill: Okay, cool. So that was the biggest challenge, gotcha. I can imagine inventing something brand-new from scratch. That’d be crazy hard. This might be related to this previous one, but what’s a recent win that you can share with us that you’ve had in your role that’s something that you’re proud of?
Beau: I recently redid the deliverable that we prepared for our client of our results. And I’m kind of a data visualization nut, and I’m really proud of how that looks. As good and as accurate or whatever your analysis is, if you don’t have a way to present it to whoever is consuming it in a way that they understand, especially if they’re not data scientists, then your analysis doesn’t really mean anything. And I feel like we’ve come up with a really good, well-designed deliverable that conveys the complexity of what we do in a simple way that non-data scientists can consume and use and understand. So I’m really proud of that.
Kirill: That’s really cool. I can imagine. It’s very interesting that you put the focus on visualization, because I completely agree. I think you’ve already touched on the importance of communication at the start of this podcast, and yeah, it's totally true, especially in something like what you’re doing which is so ground-breaking and different to what everybody else is doing. You need to get people ready. You know, with your method it almost sounds like at the very start of your presentation, you should show them a quick animated cartoon from Darwinism or something, biology, how natural selection works, and then people will be on board with your whole evolutionary programming-based methods.
Beau: Yeah, absolutely.
Kirill: Alright, cool. And what is your one most favourite thing about being a data scientist?
Beau: I think it’s the scientist part. You know, I love doing science. I love discovering new things and just going through that whole process. It has so many applications, especially powerful in business, and I think that’s a part that really drew me back into data science full-time, is finding solutions to questions and to problems.
Kirill: Okay, awesome. I can totally relate to that. And it really resonated with me when you compared the differences between finding the absolute truth in science and finding the relative truth that will help you convince the judge and the jury in law. A good contrast.
Beau: I’ve always been uncomfortable with that aspect of the legal profession. (Laughs)
Kirill: Right. And no offence to the legal professionals out there, it’s just that everybody has their own preferences, I guess, what they like or don’t like. An interesting question: Where do you think the field of data science is going? Like, from what you know, from what you’re doing, from what you’ve seen in your many lives and careers, what should our listeners prepare for to be ready for the future that’s coming?
Beau: I think it’s already happening. Much, or a lot, of what data science does is being automated. If it lessens the amount of time that I have to spend cleaning and pre-processing data, then great. (Laughs) So, I think there’s just going to continue to be more automation. But I think there’s kind of a double-edged sword with that. We need to know the reasons why the AI that we’re using or whatever is making the decisions that it does. And I think that the core value a data scientist has is in producing value from data. And that requires that communication, that visualization, the making sense of what the algorithm spits out.
I think focusing on that is really the most future-proof kind of thing for a data scientist. Is what I’m doing actually providing value? Instead of just being a research exercise, am I actually contributing to driving revenue for my company or my clients? That’s kind of what I feel like will continue to be important. The challenge now is not accessing or creating data or recording data. It’s so easy to do in the time that we live in, but making sense of it is not going to go away. It’s something that’s really important.
Kirill: Fantastic. I totally agree with that. Yeah, very, very powerful point of view that visualization and presentation and that communication, being the communicator between the insights, no matter how they’re gathered, whether automatically or non-automatically, by hand, and the people that it needs to be communicated to. That is definitely something that is going to stick around for a long time. Thanks a lot, Beau, for sharing, for coming on the podcast.
Kirill: How can our listeners contact you, find you or follow you? It sounds like LinkedIn might be one of the best options.
Beau: Yeah, LinkedIn is a great way. You can find me on LinkedIn. I think Kirill will probably have my contact info. LinkedIn is great. Also, through e-mail is good. I can give my e-mail, it’s [email protected] Either of those two ways is great. I’m responsive on either. But don’t spam me, though. (Laughs)
Kirill: (Laughs) Fantastic, yeah. Definitely, guys, connect with Beau and reach out. I’m sure there’s going to be more follow-up questions to your story. And I have one final question for you: What is your one favourite book that you would like to recommend to our listeners that can help them become better data scientists?
Beau: This is a book that’s a classic and came out way before the term ‘data scientist’ did. It’s Edward Tufte’s “The Visual Display of Quantitative Information.” I think going back to the whole idea of how your data are interpreted is the most important. I think any data scientist would benefit from reading that book. The principles are just as important today as they were when he originally wrote the book.
Kirill: Fantastic. Thank you for that. So, Edward Tufte: “The Visual Display of Quantitative Information.” Check it out, guys, if you want to be more like Beau. (Laughs) Okay. All right, thanks a lot, Beau, for coming on the show, once again, and sharing all of this. It’s been crazy and great.
Beau: Thank you, Kirill.
Kirill: Take care. So there you have it. That was Beau Walker and I hope you enjoyed today’s episode. For me personally, the most exciting part was of course the description of the evolutionary programming-based machine learning. It’s a very different space of data science and I really appreciated that Beau actually shared the advantages that it has over some of the existing approaches such as deep learning and specifically the interpretability and also the fact that it doesn’t require that much data in order for these models to be run, which can be useful in some sort of business applications.
So I hope you learned something new today and you might consider these things for your personal career. You can find the show notes at www.superdatascience.com/93. And there you can also find the link to Beau’s LinkedIn, so make sure to connect and hit him up. Of course, as we mentioned at the very start of the podcast, connections are so important, especially in this day and age. Even if you just connect with people on LinkedIn, that could lead to unforeseen opportunities in the future. You can also find the show notes and transcripts at the same URL. And on that note, we’re going to wrap up today. If you enjoyed today’s episode, we have a quick favour to ask. Just head on over to iTunes and leave us a rating or review. This will really help us spread the word about data science and get even more people enthusiastic about it. Thanks a lot for that. And I look forward to seeing you next time. Until then, happy analyzing.