SDS PODCAST

EPISODE 93

WITH

BEAU WALKER

Kirill: This is episode number 93 with Data Scientist at Liquid

Biosciences, Beau Walker.

(background music plays)

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.

(background music plays)

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.

(background music plays)

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.

Beau: Yeah.

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.

Beau: Yeah.

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.

Kirill: Interesting.

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?

Beau: Yeah.

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.

Beau: Yeah.

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?

Beau: Yeah.

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?

Beau: Yes.

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.

Beau: Absolutely.

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

beau@beauwalker.com. 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.