There is no doubt about it: the tasks that happen in data science can be complicated and unpredictable. The way we do business, sell, promote, strategize, and grow in today’s world ultimately has become data-driven. That’s why you need to have a good team that can keep up with the data. Now most organizations, from small start-ups to large-scale companies, are using the help of data scientists and data-minded folks to tap into the many opportunities that data possesses. And some companies are only just getting started. 
Today’s episode is part of a recording of a live fireside chat we hosted just a few weeks ago here in Indianapolis, where we discussed the topic of “Unlocking the Power of Data Science and Visualization.” Joining me on this special podcast episode is the VP of Data Science at High Alpha, Mark Clerkin.

Mark is an experienced data science leader with an exceptional track record of conceiving, developing, and delivering innovative processes and products. He is a mentor with Techstars and The Last Mile. Mark and his team are the architects of building statistical analysis, data pipelines, data visualization, and machine learning solutions for High Alpha’s portfolio companies.
Throughout this episode, you’ll get to hear Mark discuss the benefits of possessing soft skills, using data in startups, and bringing diversity to high performing teams. Also, you’ll get to listen to a featured interview with KSM Consulting’s Director of Talent, Louonna Kachur where she shares the fantastic energy and culture that KSM is building and the kind of talent they’re currently looking for on this episode of Powderkeg Igniting Startups.

In this episode with Mark Clerkin, you’ll learn:

  • How to make meaningful connections with other tech folks
  • The biggest opportunities in data science and visualization
  • Why startups should use data science to capture key data
  • How Data Science has become inherently diverse
  • Mark’s hope for the Indianapolis tech community and data science

Please enjoy this conversation with Mark Clerkin!

Mark Clerkin quotes from this episode of Igniting Startups:

Links and resources mentioned in this episode:

Companies and organizations:


Did you enjoy this conversation? Thank Mark Clerkin on Twitter!

If you enjoyed this session and have a few seconds to spare, let Mark know via Twitter by clicking on the link below:
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What stood out most to you about what Mark Clerkin shares in this podcast?
For me, it’s the biggest opportunities in data science and visualization.

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Related article: High Alpha’s Big Reach


Episode Transcript

Matt Hunckler 00:13
So exciting is that they’re just amazing tech companies here in the middle of the country.
And so we launched recently, Powderkeg matches, which is an easy way for you to get
matched with a job you love. This might be you, this might be one of your best friends
where last couple times you’ve grabbed beers they’ve told you about how they’re
struggling with finding something that they’re passionate about. If you know someone
that’s looking to make a move, or maybe they don’t even know they need to make a
move. Tell them about Powderkeg, it’s free slash jobs. This is not a job
board. It’s a way for people to really explore all the cultures in this tech community that
could be the right fit for them and match them with a place where they can really grow
and reach their full potential. One of those companies that we work with is KSM
consulting our friends over here. We love These folks, and they do amazing work. I’ve done
some awesome interviews with Colin that we’re going to be sharing those here pretty
soon. In case I’m consulting has over 150 people here, very talented people helping
companies large and small, solve technical problems. They were acquired last year.
They’ve got an amazing culture themselves, you can tell that just by talking to any of
them. And I’ve got someone very special to bring up here on stage. So if you could please
put your well your hands together for a warm welcome to the director of talent at KSM
consulting, we wanna capture

PK 126 Mark Clerkin Page 1 of 11 Transcribed by

Hi there

Matt Hunckler 01:42
next to you, I cando the power stance if you want me to. Yes, that would make me feel
good. I don’t know if I can hold it. I’ll do it. Alright, tell me about growth at KSM consulting.

Yeah, growth has been incredible. You mentioned that we were sold last year we
experienced 85% growth and grew in all the ways you can measure growth. So It’s super
exciting, super engaging, like all the clients, all the profit, all the revenue all the people, it’s
been really an incredible ride for us. We’re getting to do great work for United Way we’re
doing it for Health and Human Services. We’re doing it for crane. It’s been really
incredible. When

Matt Hunckler 02:16
you’ve got a data science team, they’re huge technical teams. interesting problems. You
can definitely feel the energy in the office, which is awesome. I know sometimes. I’ve heard
of other companies that have gotten acquired and they lost their energy, it seems like at
KSM is sort of amplified. Do you mind sharing a little bit about what you’ve kind of
experienced personally?

Oh, it’s been great. Like I’m so we, we were part of another organization in town. And now
it’s like we’ve been teenagers kicked out of our parents basement. And so we get to solve
all the problems. We get to go out and decide our own future. We’re picking a name, we’re
doing a rebrand. But we also we just get to build a sales team and go out and find our
own business now. We’ve been around for 10 years, we’ve built incredible relationships
which allows it Which allows us to do more work and to sort of build the sales team and
make it easy to go out and do it. We also were really invested in Indianapolis. And so
what I love about what we’ve built at case MC is that you can go out and you can see your
case emcee consultants at the grocery store at the park or at the church on Sunday. And
they’re really passionate about being embedded in this community. And so it’s almost
tangible when you’re at the office, like the energy and excitement about what we’re doing

PK 126 Mark Clerkin Page 2 of 11 Transcribed by

and how we get to sort of forge our own future and build great relationships and do
amazing work has been incredible.

Matt Hunckler 03:30
Well, and you all have been coming out in force to our events. And it’s awesome to have
you engaged in the community here. How can the powder kick community help? What
kind of roles Are you hiring for?

Yep, lots of roles. So I mentioned the 85% growth and 91 new people last year, which is
incredible. We have some rock stars here today. So you should talk to them and find out
about the work that they do. We’re looking for lots of roles. So we’re looking for data
scientists, of course, data architects, data engineers, all the data app dev people. We’ve
got Some basic project managers and consultants, so just some PCs, if you have good
project management skills, there’s a place for you there too. We, with the growth that
we’re experiencing, we’re bringing on tons of skill sets.

Matt Hunckler 04:12
Cool. And if you want to learn more

case, I’m consulting careers page, or again, talk to any of my resident rockstars. They’d be
happy to help out to

Matt Hunckler 04:19
grab a beer with them after the after the talks. Yeah, and we wanna I just want to say how
grateful I am that your team really values culture, not just internally there, but then
plugging that in also into the culture of the tech community here in Indianapolis. So thank
you so much. Great. Thank you for that for the one. All right. Our final guest I’m super
excited to have this conversation. I learned so much in my lunch with him a week or two
ago. I’ve heard so many great things from the partners at high alpha, who, for those that
don’t know long and storied history of helping create this powder keg community going as
far back to 2009. helping us get our brand I’m not wearing my powder keg shirt. Thought

PK 126 Mark Clerkin Page 3 of 11 Transcribed by

it was helping us get our brand helping us find those people to connect with and really
grow this community. This person is not just helping his team, which architects building
statistical analysis data pipelines and data visualizations, and machine learning solutions
that high office portfolio companies, but they’re helping companies even outside of high
alpha. So companies like pattern 89 back there. I’m not sure if you got a demo of what
they’re doing demand jump. He’s also a mentor to tech stars and the last mile, please help
me welcome to the stage VP of data science at high alpha Mark clarkin. Mark, it’s really
hard to pick just one story to try to dive into but what I want to start with is, you left us
here in Indianapolis. Good news is you’re back, although we’ve cut your microphone
because when they heard that you left Indianapolis, they were just like, nope, you’re done.

I totally I understand, you gave a little hat tip to the partners at high alpha, thanks to
them and what happened with exact target and Salesforce here, the tech community is
completely transformed from where it was 10 or 15 years ago. So for me coming out of
school, I really was hunting for the most technical job that I could find, which took me to a
bigger market. It took me to Chicago, and then things went from there. But now there’s
such a robust tech scene here that it’s easy to come back in and plug in and have
amazing jobs or for the people who are coming out of university right now or people are
making midlife career changes. There’s a lot going on,

Matt Hunckler 06:36
what was that like making that decision? was it was it a no brainer? Or did you really have
to kind of weigh the options?

I’m not sure To be honest, I just it was a perceived kind of thing where I thought there were
bigger opportunities externally, maybe there were here. I mean, obviously, that was right
in the middle of the rise of exact target and other companies like interactive intelligence
and a lot of other folks who’ve been successful. So it might have just been a perception,
but I do definitely think that perceptions change now.

Matt Hunckler 07:02
So you definitely work with a lot of data scientists, a lot of tech teams, but you also work

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with a lot of CEOs, non technical folks, can you talk to me a little bit about why it’s
important, whether you’re technical or not to understand at least a 10,000 foot view?
data science?

Well, absolutely. I mean, one of the things that I’ve learned at high alpha and come to
appreciate a lot is the customer. I came from quantitative hedge fund before this, and we
were like proprietary money, so we didn’t even have extra external partners. But now
learning how to interface with customers how to talk to partners, really, kind of emphasize
the importance of being able to both speak plain English and then speak math. And so
being able to bridge that gap is incredibly important because, you know, any business is a
team sport, and everybody needs to be on the same page in order to win.

Matt Hunckler 07:53
So for those that are non technical, not looking to become a data scientist, what would
you encourage them to do? Get to in terms of at least a certain bar of knowledge ability
of data science.

Frankly, I think it just happens naturally by being around data oriented folks within your
organization, I would just say be very transparent about what you do know. And what you
don’t know. I say this all the time. Yeah, I know, data science, but I know nothing about
sales. I know nothing about customer service. So the folks on the team that do that, like
I’m all ears, and then I hope that it’s the exact inverse when we start talking about
Algorithms and Data distributions and things like that.

Matt Hunckler 08:30
And any advice to the people who maybe are technical are in data science, how to put
that into as you put plain English?

Yeah, we really value EQ in our team as well as IQ.

PK 126 Mark Clerkin Page 5 of 11 Transcribed by

Matt Hunckler 08:44
Tell me what that means.

Sure. So your emotional quotient, you know, it needs to be as high if not higher than your
intelligence quotient. I mean, we have some really smart folks that work on our team with
a lot of experience but frankly, if you can’t interface with others well, and be
approachable, be you know, kind Be willing to say things a lot. I mean, communication is
the number one skill set you should have as a data scientist, then maybe you’re better for
a research role.

Matt Hunckler 09:08
I absolutely love how you put that interface with other people. Technical, very faded very
data science way of Yeah, talking about building relationships. Yeah,

I do I do my best to speak plain English, not just pure

Matt Hunckler 09:22
engineering. No, I think that’s great. Well, what are some of the things that you learned
from the finance and hedge fund experience that have served you really well, now helping
data science with startups to large enterprises? Sure.

The fund that I worked for first, it’s called jump trading. It’s a very, very, very, like quiet and
prestigious fund. And they were really smart and ahead of the curve in terms of how they
captured data, or at data centers around the globe. So we would park right next to these
exchanges. We would capture every single bit of data that came out of them with the
intention that we would be able to use it in the future and so This is mid 2000s. There’s no
such thing as data science. People don’t talk about that. The neural network revolution
hasn’t happened yet. But we’re starting to capture all this data. And yeah, we’re building
some basic algorithms around it. But really, it was the fact that they had laid this whole

PK 126 Mark Clerkin Page 6 of 11 Transcribed by

foundation, that for the next generation of folks who came in right after me, who were the
super quants, and really how I got my education, it was like their playground to like play
on top of and so that’s what I try and instill with the high alpha companies is, we do a lot
of things fundamentally on how we capture data, how we architect moving that data
around how we make it accessible within the organization. And then that allows myself,
our business analysts, people on our team, you name it, the gives them the ability to
liberate that data and turn that into insights for our customers and then the flywheel
starts to spin. You get some wins, you start to bring in the data science team, then you
really scale up and you go from there.

Matt Hunckler 10:57
I think that’s great. Well, and I I think you mentioned high alpha and I just kind of assume
people in Minneapolis know, but I gotta slow down for a minute. Remember that we have
over 5000 people outside of Indiana listening to this podcast watching the show. Can you
maybe explain that at a high level? What high alpha does?

Sure, yeah, I’d be happy to so high alpha is a venture capital studio and fund. The fund is
what you think of is in typical VC world, we invest in b2b SaaS companies, series, a maybe
Series B kind of investments. But the studio is where I spend the vast majority of my time
and that’s where we conceive, launch and scale b2b SaaS companies. So to date now
we’re on our second fund, and we’ve started approximately 18 companies internally. The
vast majority of those are based here in Indianapolis, but that’s not exclusively an Indiana
thing. And a lot of those companies are going on to do really great things. they’ve raised a
ton of money, we’ve created 500 jobs. Again, most of those being here locally. So yeah, it’s
a pretty fun thing to say absolutely

Matt Hunckler 12:04
lots of those leaders on the powder keg podcast as well. So we’ll link up to those in the
show notes for sure. You mentioned the startups. Talk to me a little bit about why startups
or even enterprises starting a new project need to be thinking about data science, from
the very beginning of the project?


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Well, that’s a good question. You know, when we turn it around and tell you some
anecdotes of what I’ve seen happen, a lot of the times after the fact a product person or
an executive or whomever within the organization will come up with a really great idea, a
really great new product for us to bring to market or a modification of a product we have.
And the failure point there that almost always occurs is we haven’t been capturing that
data. You know, nobody from the beginning of a project can see over the horizon with
2020 vision and know exactly all the pros and cons that are going to play out. But if you
have enough pattern recognition and you’ve gone through it enough times, you kind of
know Some things that you should be doing in order to have the widest base to stand to
stand on in the future. So to answer your question for the startups, that’s kind of the
competitive advantage that my teams give the startups within the powerful high alpha
portfolio. And beyond is, you know, that expertise to know how, when and why we should
be capturing things so that way we have the most opportunity down the road.

Matt Hunckler 13:21
So give me an example of what that might mean. With like a, you don’t have to say what
company it is. But could you give me like maybe a real example of a company that
maybe didn’t think about something early on that later on needed to kind of rethink

sure it happens all the time. By the way, I’m supposedly an expert at this and I still mess it
up. Pattern a nine a company that’s here. We are embedded within pattern. 89 Charlene,
a data scientist on our team really runs data science for pattern 89 it’s been an absolute
honor and pleasure to work with them. What pattern 89 is is a digital optimization, digital
advertising optimism platform. So we analyze the creative, the media, the content that
you put into an ad on Instagram, or on Facebook or on Google, in order to help you
optimize what you should be messaging, what kind of images you should be using, etc.
Well, we pull your historical data from Facebook, which our customers give us permission
to do. And then we analyze that to find patterns and to make recommendations in order
to improve your return on adspend. Well, there’s a lot of stuff you can get back from from,
from Facebook, there’s also a lot of things that we need to really be thoughtful about,
about how we capture media and how we tag it and what we do with it. And there’s just
tons of implications there. And so inherently, we did a really good job of that from the
start, which has given pattern and the base to stand on that they do today, and they’re
being tremendously successful now. However, I was literally just in a meeting this week
where we were talking about, oh, we would like to run a specific type of analysis for one of
our customers. And everybody realized we haven’t been capturing this data. So now we

PK 126 Mark Clerkin Page 8 of 11 Transcribed by

have to go back to the API, you know, work with our engineers, they’re going to start
capturing it. But it’ll be a few months now before we can have any meaningful insights
from that data. So that was kind of high level. But that that’s very common. And that that
that’s exactly what helps

Matt Hunckler 15:16
make it a little more concrete. And especially if you have a chance to go over to the demo
tables after the talks. pattern. 89 has a really cool demo over there. So make sure you talk
to Ian. Yeah, make sure you talk to Ian. I’d like to dive in a little bit deeper, because one of
the things that you pointed out, you know, Charlene, who’s helping out at pattern 89,
you’ve got a really diverse set of backgrounds on your team. And one of the things I’ve
heard you say before is data science is inherently diverse. Hmm. Can you tell me what you
mean by that?

Yeah, I honestly, I really don’t know the genesis of that. But in my career, I’ve been so
fortunate to work with people from literally Europe, Africa, Asia, South America. Currently
on my team we have Charlene is originally from Singapore. Marina Maria is from
Philippine descent, like it’s just amazing, the different cultures that are interested in data
science. And also to you know, we’re really lucky that we have two girls on our team, you
know, we’re really trying to do more in the community for inclusion, inclusion and diversity
and Girls Who Code and you know, thinking about those kind of initiatives. So really, with
data science, it really just comes down to are you creative? Are you tech technically
oriented? Are you human? Great, what’s worked together?

Matt Hunckler 16:37
Well, I think the data supports that too, you know, diverse teams that are inclusive of
women, people of different backgrounds. Even people from maybe underrepresented
socio economic backgrounds can help solve problems better, as long as you have that
inclusivity built into your culture. That’s true, which of course has to be a priority. But you
Eric and your team of high alpha, one of the things I did not know about high alpha is that
you actually have your own consulting arm that helps not just studio companies. Can you
tell me a little bit about some of the work you’ve done there?

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Sure, definitely. So we have in data science, there’s there’s generally a finite set of
problems that you can solve, or at least some very common ones. So once you build up
that muscle, and you’ve done it a few times, and you know exactly the routine to go
through, you’ve got this kind of IP. And so our group started to say, you know what, we
could leverage this and utilize it outside of the portfolio. So, as I mentioned at the
beginning, steady or high alpha is both a studio and traditional venture capital fund. Well,
we’ve started partnering with some of the folks that we’ve made investments in to
leverage some of that technology, I can give you a specific example. Rise science is one of
our portfolio company, I’m sorry, our studio now. I’ll do it again, portfolio companies and
they are A sleep tracking app that tracks your sleep and then correlates that to your
performance at work. So up until recently, what they’d been doing is utilizing peripherals,
your Fitbit or your you know, any other kind of watch, you might have to know if you’re
asleep or awake. That doesn’t scale forever, though, right? Because not everyone has a
peripheral to be able to infer if you’re asleep or awake. So we’ve been working on
leveraging some, some vision models that we created internally, and then using them for
that audio signal processing, to where now right science can just you can turn on the app
and it can just listen to you while you sleep next to your bed and infer with great accuracy
if you’re awake or asleep throughout the night. So that’s just an example of leveraging
some stuff we built internally, changing it just a little bit and utilizing it externally.

Matt Hunckler 18:52
My fiancee is gonna be mad at you. They told me about this because I literally was like,
Oh, I can’t use it because I’m out of wrist. I’ve got whoop on my right I’ve got an Apple
Watch. On my left, but now I can just use my phone. That’s right, we got you covered.
Third thing for me to track sleep. That’s great. All right, I’m gonna go home and download
it today. So finally, I know we could talk forever about data science. And we can continue
this conversation after the talk. But for those that want to continue to learn about data
science, are there any blogs or podcasts or even people to follow on Twitter? whatever

Yeah, it’s amazing how much information is out there. Now, when I started, it was really it
was as dry as could be. It was just papers. And now you know, there’s towards data
science, which is a medium blog posts. You know, there’s all sorts of just technical blogs,
like if you’re reading Uber engineering, or you know what’s going on Airbnb, there, tons of

PK 126 Mark Clerkin Page 10 of 11 Transcribed by

podcasts. So this week in machine learning is really an AI is really an interesting podcast.
and many others that just don’t come to mind right off the top of my head. But the honest
truth is if you’re searching for information, you’re it’s not going to be hard to find it.

Matt Hunckler 20:00
So finally, my last question is, what is your hope for this Indianapolis tech community as
we continue to grow and its relationship with data science? Sure,

it correlates with my story that we started on at the beginning of who you know why I left.
And now I came back, the tech talent, the tech scene, and the tech talent is totally robust
here in Indianapolis. However, we do need to go through this second wave of retooling
and, you know, upping our skills and that’s all going to be oriented around data. Now, you
don’t have to be a pure data scientist, you could be business intelligence, you could be an
analyst, you could be a data engineer, anything. But being data in numerically competent
is a skill set that we must have to go forward and be competitive on a global landscape,
not just within the United States.

Matt Hunckler 20:46
Let’s do it. Let’s, let’s wrap it up there and get to connecting and learning from each other.
Let’s give mark a huge round of applause for sharing his story.