Dr. Dan Hudson: Analytics & Automation Hiring Manager

In this episode, I have a great chat with Dr. Dan Hudson, Director of Advanced Analytics and Intelligent Automation at Guidehouse. Dan and I having a wide ranging conversation, to include: the veteran-friendly culture at Guidehouse, Dan's military background and personal data science journey, insights from his experiences as an analytics and automation hiring manager, the future of our field, and the skills you should focus on to remain in-demand over the long-term.

FEATURED GUEST:

Name: Dan Hudson

Email: danhudsonphd@gmail.com

LinkedIn: https://www.linkedin.com/in/danhudsonphd/

SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!):

Donate: https://vetsindatascience.com/support-join

EPISODE LINKS:

Guidehouse Veteran Career Info: https://guidehouse.com/careers/veterans

Guidehouse Veteran Opportunities: https://careers.guidehouse.com/veterans/jobs

Certified Analytics Professional (CAP - certification): https://www.certifiedanalytics.org/for_professionals.php

Choiceology (podcast): https://tinyurl.com/22tctk6b

How to Decide (book): https://tinyurl.com/tk5p6vmf

Thinking in Bets (book): https://tinyurl.com/28ncvbnu

PODCAST INFO:

Host: Ted Hallum

Website: https://vetsindatascience.com/thedatacanteen

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-data-canteen/id1551751086

YouTube: https://www.youtube.com/channel/UCaNx9aLFRy1h9P22hd8ZPyw

Stitcher: https://www.stitcher.com/show/the-data-canteen

CONTACT THE DATA CANTEEN:

Voicemail: https://www.speakpipe.com/datacanteen

VETERANS IN DATA SCIENCE AND MACHINE LEARNING:

Website: https://vetsindatascience.com/

Join the Community: https://vetsindatascience.com/support-join

OUTLINE:

00:00:07​ - Introduction

00:01:08 – Catalyst for Dan's recent move to Guidehouse

00:03:53 – What's Guidehouse?

00:04:18 – The culture at Guidehouse

00:06:47 – What makes Guidehouse a veteran-friendly employer?

00:10:17 – Current veteran-focused employment opportunities at Guidehouse

00:11:24 – What is the Advanced Analytics and Intelligence Automation practice?

00:13:40 - Dan's military background and personal data science journey

00:27:18 – One of the beauties of the data science space

00:28:38 – Insights from Dan's experiences as an analytics and automation hiring manager 

00:54:24 – How you can request mentorship

00:48:44 – To generalize or specialize? That is the question!

00:41:47 – If you specialize...to what degree should you specialize?

00:57:13 – Everything you might want to know about the Certified Analytics Professional (CAP) certification

01:02:53 – Are we approaching a point when we'll all need to become software engineers?

01:06:06 – Dan's recommendations for data science-related podcasts and books

01:10:15 – Dan's preferred means of contact

01:11:15 – Farewells

Transcript

DISCLAIMER: This is a direct, machine-generated transcript of the podcast audio and may not be grammatically correct.

[00:00:07] Ted Hallum: Welcome to the data. Canteen, a podcast focused on the care and feeding data scientists and machine learning engineers who share the common bond. If U S military service. I'm your host, Ted Hallam. Today I'm joined by Dr. Dan Hudson. Dan's a veteran of both the Marine Corps and the Navy. He has a background in aerospace engineering.

Holds a decision science focused PhD from the John Hopkins university and currently leads the advanced analytics and intelligent automation practice and God house today. Dan and I will talk about this recent career move to God house and the culture that drew him there as a veteran. We'll also talk about the traits, characteristics, and credentials that get Dan's attention as a hiring manager.

And finally, we'll talk about the future of our field and the skills you should focus on with an eye towards the future. I hope you find the conversation as informative.

Dr. Dan Hudson, man. I'm so excited to have you here on the data canteen. We have been trying to make this happen for how many months?

[00:01:02] Dan Hudson: At least a couple it's been maybe two to three months.

It's taken some time.

[00:01:08] Ted Hallum: There were multiple schedule conflicts and it was just difficult to get the stars to align. But here we are, today is the day. Now from what I understand, you just had a pretty big career move. And now you're the director of advanced analytics and intelligent automation at Gatehouse, right?

[00:01:23] Dan Hudson: That's right. Yes. That only, that only happened about a month ago at this point.

[00:01:31] Ted Hallum: Now tell us a little bit about kind of what led you that direction. Usually, at least in my case, like a career move is sort of, the catalyst is I've gotten interested in something when I want to be able to pursue it or whatever.

What, what led you to go to this new role at guide house?

[00:01:46] Dan Hudson: Oh, so many factors. I mean like any big decision on life, there are many factors competing objectives that you have to consider and weigh in, in these big decisions in life. And I'll, I'll just highlight a couple of them for you. I mean, first and foremost I had the opportunity to work with people at guide house in my previous role.

So before coming to guide house, I served as the chief technology officer and chief decision scientist at reef point group. And in that role, I partnered with many people at guide house members of the leadership team, partners, directors, people on the technical staff. We work together, you know, both firms.

Are in the consulting space primarily serving on the reef point group side, primarily serving federal government clients on the guide house side guide, how to serves both federal clients and commercial clients. But I had the opportunity to engage with people at guide house at my previous role, as we were working together to support customers within the federal government contracting space, primarily within the department of veterans affairs or VA and within the department of defense, especially within the military health system.

And so through that work, I gained a deep understanding of the kind of people that work here at guide house, the customers that they serve, their work ethic, their culture, and all of those things really resonated with me. And so. When it became time for me to move on for, for various reasons, I had served in my role at reef point group for two and a half going on three years.

And I was at a decision point to evaluate where I was and decide whether I wanted to go on and do something else or, or stay where I was. This was a wonderful fit for me.

[00:03:53] Ted Hallum: One of the big things behind the veteran's data science machine learning community and the data cantinas podcast is helping veterans who want to get in the field of data science and machine learning, make that transition and then find good meaningful employment out in industry and in getting ready for this episode, I did a little bit of research on God house, just so you know, I could speak intelligently to it while you and I are conversing here.

And I was pretty impressed. I mean, it's a large consultancy, like 10,000 plus people I saw. They've got a number of sort of endorsements in terms of their culture and how great of a place it is to work, but one of them is actually called the great place to work certification. Can you just speak to that a little bit?

That sounds interesting.

[00:04:37] Dan Hudson: There are various elements that go into it. There are surveys that are conducted amongst the, the members of the the staff and managers and leaders at the organization to get a feel for what it's like to work there.

You know, not all organizations out there are able to meet that mark and earn that certification. So there's something special that's going on here at guide house that enables that organization to earn that designation. I can tell you I've been in my role here at guide house for less than a month, but I've had the opportunity to work with people here for the better part of two years, two and a half years. I have a good understanding of the way that people work here. And ultimately that's what culture is all about, right?

It's it's not just platitudes and what is put out there. As you know, this is who we are as an organization, and it's really fundamentally about, you know, how things work day to day and in my previous role at reef point group and the role that I'm in today, I've been able to ascertain. What it really means to be a part of the culture here at guide house that contributes to that designation, that designation of a great place to work.

You know guide house uses a values framework to guide the work that they do and supported their mission and vision. And it's called rise. It's R I S C. So the is squared and it stands for respect, integrity, innovation, stewardship, and excellence. And those are a set of core values that really aligned with my set of core values.

And I think that's what it's all about, right. Is finding organizations where the things that are important to that organization are the same things that are important to you. And that played a critical role in my decision to come to this organization.

[00:06:47] Ted Hallum: Now of course, you're a veteran, I'm a veteran.

This podcast is geared towards veterans. And as I explored God house's website, if it's okay with you, I'll bring it up here in just a second. I realized that it seems like a very veteran friendly company. I found that there was an entire website dedicated to just pursuing a career options at Gatehouse.

And then there was a separate web page that shows all the different actual job, current open job opportunities that they feel like are geared towards veterans. So I bring that up here for us real quick.

Yeah. And as, as I kind of scroll through here, I would just love to hear, you know, as you were evaluating guide house for yourself, and then when you think about veterans more broadly, what things do you think make it a great place for veterans? Aside from the things you mentioned earlier that make it just a great place to work for general people.

[00:07:44] Dan Hudson: Yeah. Great question. I mean, I think first and foremost, for those of us that have served, it's important to find work that has meaning, right. We, we chose to raise our hand, volunteer and serve for a reason. And that was. Some sort of impact, you know, the work that we were doing, we knew was going to have a meaningful impact in whatever role we were serving within the armed forces.

And for me, that was important when I was leaving the military. A lot of us. Struggle when we get out of uniform finding an environment that we can work in, where we felt the same sense of satisfaction reward out of the work that we were doing day in and day out. I mean, the reality is so many of us spend so much time working.

It's so important to be able to connect that work that we do with something that is meaningful to us. And so being able to go to companies, to organizations that we know, and you know, this is me speaking personally, and as an individual, I wanted to find something that I knew was going to be impactful and continue to support the same kind of mission that I cared about when I raised my hand and served in the military before and anticipate that we're going to talk at some point about that part of it as well.

So guide house guide house you know, an important client base at guide house, like I've mentioned before is the department of veterans affairs, the VA that organization that is so important to making sure that those who have served realize the benefits and services that come along with the sacrifices that they made in serving the country that we live in.

And in addition to the VA, we support the department of defense not only on the healthcare side, which is an important segment that I support, but in the bigger DOD space, you know, supporting the war fighters that are on the front lines. So being able to make a meaningful impact, a tangible impact on those organizations that continue to support those who have served and are continuing to serve.

The country that we live in, those were important elements informing my decision about where I wanted to work on the civilian side.

[00:10:17] Ted Hallum: Absolutely real quick. I'll flip over. I mentioned that God house has a webpage geared specifically towards veterans and those who are transitioning from the military and positions that they think are well well geared towards veterans.

And as I scroll through. I know, of course the military has offices, operations, research systems analysts. So there's an operations research analyst position here. But then as I went down further, there's some open data scientists, opportunities, analyst opportunities. So if you're listening to this podcast and it's, you know, sometime around the summer of 2021 still then you should go out to this website, I'll throw up the link to it real quick.

There you go. That's where you need to go because they have some opportunities right now that if you've already done some upskilling or you came out of the military with some of those analytical skills, You might be able to get to work at guide house right away. So speaking of getting to work at guide house while I've got the web browser up here, I'll flip over, and this is the page for the actual practice that you lead there at guide house.

So if people want to go out and check that out, here's the link to that. And dad, I'll just turn it over to you for a second to talk about, you know, what the advanced analytics and intelligence automation practice here at God houses is all about.

[00:11:38] Dan Hudson: Yeah, thank you. I mean, I think before I dive into that, it's probably worthwhile to take a step back and talk a bit more about guide house in general, because I think that provides some important contextual information around how, how we fit into the bigger picture.

So you talked to some extent about this in the beginning, you know, guide house is a large consulting firm. We're global in nature. We have 10,000 plus employees. We support both the commercial and the public sector segments. And th the business is organized around a number of verticals or segments.

You know, this includes defense national security, financial services. Health care or health in general, which includes both healthcare and public health and life sciences and others. So those verticals are the core of the business. Now. The area that I work in at guide house is called advanced solutions.

And we're one of two horizontals that support the entire business. And what that means is that, you know, we bring capabilities experience to all of those different verticals. We support them. This is all about taking the advanced analysts in, in my case, advanced analytics and intelligent automation capabilities and infusing them augmenting the capabilities of those individual segments to ensure that we're bringing the latest and greatest the state-of-the-art technologies and solutions, those things that are disruptive and turning that disruption across all of those different segments that I was highlighting before.

[00:13:40] Ted Hallum: So I appreciate you covering what God house is sort of as an organization, as an institution, as a business, and then specifically the practice there that you lead. I think that really gives people a good context for where you're coming from. And, but before we go any further I want to take a step back because before you were the director of this analytics practice there at guide house, you had a whole life that led up to that point to include, you know, we already spoke about the fact that you're a veteran of both the Marine Corps and the Navy and I'm sure that you had just an, a fascinating data science path that we won't be able to cover in its entirety.

During this short data canteen episode, but I'd love to hear just a snippet about, you know, where you came from your interests growing up, whether you were always into stem or you had to grow into that passion how you progress from college to the military all the way up to kind of where you are now.

Because that, that kind of background gives people a template that a lot of times they can relate to it resonates with them, and then they say, wow, Dan did that. I can do it.

[00:14:46] Dan Hudson: as a fan of the Austin power series, I'm reminded of the very first one of those where Dr. Evil was given the stage to sort of talk about his background and it's like, where do I begin? You know? So I'll, I'll go back to the beginning just to share you share a little bit about, you know, who I am and where I came from, I grew up in California, was born in Southern California, spent most of my childhood in the San Francisco bay area.

But toward the, the high school years, I was up in Northern California and Nevada around lake Tahoe. That's where I went to high school. I come from a blue collar family, nobody before me in my family had ever gone to college. So when I graduated from high school and I had held at least one job at times two or three jobs throughout high school, I knew that I wasn't quite ready to dive into college at that point.

And so I decided to go into the military having some awareness of the benefits that come from serving and the kinds of opportunities that would be afforded to me to be able to go to college at some point after I enlisted, I had originally planned on going into the Navy, like my grandma, grandpa, and my great-grandpa my great grandpa was a submariner during world war II.

So I had originally planned on going to the Navy when I made my appointment to talk to a recruiter, I didn't realize at the time that the Navy and Marine Corps shared a recruiting office. So I had planned to go to the recruiting office to join the Navy. But a staff Sergeant in the Marine Corps.

So when I showed up, I was very surprised. I was going to be respectful and listened to what he had to say, but at the time I was like, there's no way I'm going into the Marine Corps. I'm going into the Navy. But 30 minutes after listening to what he listening to what he had to say and having the opportunity to sort of chart my own course, I was sold on going into the Marine Corps.

And so I enlisted in the Marine Corps. I ended up serving for about four years as an electronics technician. So You, you talked about some of my interests growing up leading up to that. I had this vision of either being a doctor or an astronaut two very different things in some respects. But they share some commonalities as well.

I, I knew that I liked tinkering. I knew that I liked to explore different, different things. And, and so that, that was what I had seen for myself in my younger years. So I became an electronics technician in the Marine Corps. So gained some exposure relatively early on to electronics, electronics engineering, tinkering around with various electronics equipment in the, in the Marine Corps radios, things like that when I was in Okinawa.

I discovered that there was a pathway for enlisted Marines to go to the Naval academy. And so I started pulling the thread on that. My, my battalion commander in Okinawa was a Naval academy graduate himself. And so he helped me navigate that process. We put together my application package and ultimately because I had been removed from an academic environment for a few years at that point, I was accepted for admission to the Naval academy, preparatory school in Newport Rhode Island.

And that's a nine month program that is ultimately designed to help individuals sort of bridge that gap. Whether there has been a break, like in my case, in being in an academic environment, or maybe there are individuals who didn't Excel in those areas and in high school, it prepared them for going into the rigorous environment that they were.

Going to encounter at the Naval academy. So I finished that up, went to the Naval academy. And at that point I mentioned earlier on, I wanted to be either a doctor or an astronaut. When I went into the Naval academy, I was laser focused on the astronaut pathway. So I majored in aerospace engineering there.

And when I graduated and took my commission, I ultimately chose to take my commission in the Navy instead of the Marine Corps, a decision that my Marine Corps buddies and many of your listeners who are Marines are probably going to give me some flack for because they don't understand it. I had this vision for being out in space and I thought, what better.

The space in these isolated, highly controlled environments than to spend time in the under sea environment here on earth. So I became a Navy diver and submarine warfare officer and served in that capacity for a number of years. So I I'll, I'll pause there for a moment because there's another break.

I think like you, I've taken this winding path to get to where I am today and probably like many of your listeners. I reached that critical juncture when I was getting ready to leave active duty and make a decision about what I was going to do next. But there's more to follow there. I'll pause there for a moment to see if there's anything you want to talk about about that point.

[00:20:16] Ted Hallum: Awesome. Some takeaways I noticed from, from what you said, I gathered that you were just natural. Curious and you'd like to tinker. So in my mind, those are two key traits, quintessential traits of people who get into data science and just love it. And then I think probably, and I'd love to hear your thoughts on this.

Obviously it's not a requirement. I don't have a background in engineering, but I know lots of people in the data science field who did start out in some flavor of engineering. And as I watched them, I get the sense that that background engineering, those engineering fundamentals really helped them in their profession as a data scientist or machine learning engineer or whatever.

[00:20:57] Dan Hudson: Yeah, I I'd agree with you. Completely that, that foundation is so important and in every job that I've held, one of the things that has been so important to me is to obtain a firm grasp of the fundamentals, the first principles that can help guide decision-making. It was the same thing with being on a, on a nuclear powered submarine.

It was all about understanding the first principles, how the nuclear propulsion plant works, the basics of tactically, employing a separator. Those first principles are so important when you find yourself in situations that. Aren't covered by procedures. You encounter something novel. It's those first principles that fundamentally ground you and help guide your decision-making in dealing with situations that you haven't encountered before.

And so it it's been a part of every aspect of my life. And I think as you talk about the engineering curriculum and the basis for, for where I come from, that was, that has and continues to be so important in the work that I do today. The, the one thing that I would add to that. And this gets me into the next chapter of my life.

And I won't spend too much time on the next part. I'll quickly get to where I am today, but when I left active duty at that point in time, I had started a family. I was married. I had a one-year-old son and was faced with the decision about what to do next. I decided to abandon my desire to be an astronaut and decide.

Pivot and go back to the medicine route, being a doctor that that was the pathway that I thought was going to be best from a family perspective at that point. And so when I left active duty, I went into medical school at Johns Hopkins university. And after completing my first year there, life happened again, my wife got deployed to Afghanistan.

I found myself being a single parent to he's now a 14, but at the time was about two years old. And so I took a step back, did a research here. And it was a great time because healthcare at that moment was very interested in learning what kinds of insights, principles, best practices that they could take from sectors like nuclear power or commercial aviation, and infuse them into the healthcare setting to help improve the safety and reliability of caring for patients because too many patients, even today continue to die from medical errors that occur.

And so that's what I spent my research here doing. Fast forward due to life circumstances, I was faced with another, another decision. I ended up deciding to voluntarily withdraw from medical school. And that's when I went to work for the U S nuclear regulatory commission, the federal agency that regulates commercial uses of nuclear materials, think of commercial, nuclear, power plants uses of nuclear materials and medicine and industry.

And that is where my interest in data science really took off because we were building. These probabilistic risk models, trying to understand what could potentially go wrong at these nuclear facilities, how likely it was to occur. And if it were to occur, what would be the consequences in terms of the public health impacts the impacts to the environment, the impacts to the economy.

All of these things rolled into one and I was fascinated by it. And I was fortunate enough to have the opportunity while I was working at the nuclear regulatory commission to go to graduate school. And so I tailored my curriculum in graduate school to focus on a couple of different aspects. Trying to bring together the intersection of technology and public policy and data science was fundamentally underpinning.

All of that. It was how do we leverage insights from data to help make better policy decisions? And so that's what I ultimately focused on in my research in graduate school.

[00:25:23] Ted Hallum: So earlier we talked about, you know, those, those two traits, one of them being curiosity, obviously you embody that to like the nth degree with, with aspirations and interests ranging from wanting to be an astronaut to wanting to be a doctor.

And then just kind of through the life, circumstances, you found data science. I think that's, I think that's somewhat typical of people in data science and machine learning. Is that we tend to be curious people. We tend to be people with a broad array of interests and aspirations. And so I think that there's a really high probability that there are listeners to this episode like yourself, who maybe they're interested in data science and machine learning, but they're interested in a lot of other things.

They're passionate about a lot of other things. So I'm going to ask a hard question, but I think it'll be a helpful question for our listeners and that is you know, you didn't get to become an astronaut and you're not a doctor today. You pursued the field of data science and now you're a leader of quantitative teams.

Amid all those fascinating interests that you have and those things that you and I aspire to do, how fulfilling have you found data science? And do you, are you as happy doing that as you've been doing other things?

[00:26:41] Dan Hudson: Wow, that is a hard question. One that I probably don't know the answer to because I was only able to scratch the surface when pursuing my interest in medicine.

And haven't really had the opportunity to explore what it's like to be an astronaut and to be out in the space environment. So it's hard to say I can tell you that I find that the work that I have been doing in the data science world to be incredibly fulfilling because it touches upon, and this is one of the beauties of being in the data science space.

It's relevant to so many different industries or domains, you can tackle such a wide range of problems. And in many cases finding solutions to those problems can touch so many people. So you can have impact at scale which I don't think a lot of people get to do in, in their day-to-day work.

And it's great to find meaning in that, right? So that's, that's, that's one of the beauties of the data science space is that the scale that we operate on the, the number of people whose lives are impacted by the work that we do is so broad, so big that I find incredible meaning in that it's, it's so different.

And you know, when I think about the contrast between what I do today, versus what I, what I would have been doing as a. As a physician, you know, as a physician, you're touching individual patients, you're having an impact one patient at a time, but in the data science space, the models that you develop, the decisions that you inform, you have the ability to impact lives at such a massive scale.

And I find that to be incredibly rural.

[00:28:38] Ted Hallum: A second ago, I touched on you now being a leader of quantitative teams, that's the core function that you serve there at guide house. So I'd like to kind of pull that thread and tease out some insights from your perspective, as somebody who manages AI analytics, machine learning teams and folks like that.

You know, a lot of people are listening to this podcast because they're thinking about up-skilling, they're in the middle of upskilling that. Herculean effort of trying to get the first data science job or first data analysts job is still in front of them. They're, they're waiting to slay the giant as it were.

And so a lot of them, I think, want to know that the traits that they need to develop or that they need to, you know, honestly be introspective, you know, and say, are, are the seeds of those traits even there. So for people that you've seen doubling your teams and be the most effective contributors, what are some of the core traits and characters sticks that you just see commonly come up again and again, in these successful people?

[00:29:46] Dan Hudson: That's that's a great question. And. Honestly, I feel like the population that is going to be listening to your podcast, watching the video the people who have served the veterans that are interested in entering the data science and machine learning space. I think they have those attributes or traits that I think are so critical to being successful in this space.

Obviously the technical skills those, those are important, right? But the individuals that I see being really successful in this environment, They really have this wonderful balance between the technical skills or hard skills and the so-called soft skills and a wonderful PR person that I follow on a regular basis.

A thought leader in this space, Cassie causer CAAAV, who is the head of decision intelligence at Google. She doesn't like the term soft skills. She prefers to refer to them as those skills that are really difficult to automate but whatever label you attach to it that mix between having the technical chops, which are important in this space and the soft skills, those, those things like critical thinking, problem, solving ability, creativity.

Communications. Those are the things when somebody is able to bring those to a team that is really the recipe for success. And I think veterans who you know, really those who serve in our military, they're, they're a microcosm of the country that we live in. There's a lot of diversity of background experience and thought amongst individuals who served in this country and fundamentally in the solve problems creative thinking, innovation, that is all part of this successful military enterprise that we have here in here in the U S and you bring all of that when you're coming into the data science space here.

So I guess I'd say that. Yo people who are listening, who are watching you have the fundamental ingredients of what it takes to succeed in this environment. For those of you that may not have the technical chops, there's a gap to be filled there, but there are so many mechanisms out there for filling that gap, you know, to use the term up-skill where, where it's needed, but you have what it takes to be successful in this environment that we work.

[00:32:40] Ted Hallum: I love what you said about soft skills thing, the skills that are difficult to automate. I think that that kind of caused me to think. I w I realized, as you were saying that not only are they skills that are difficult to share the skills that are difficult, in some cases, maybe impossible to teach technical skills, you can teach, people can go learn those soft skills.

Often times it's more of a sort of, you have them and you can develop them when you were young or you didn't. And not to say that you couldn't cultivate them. I think it's much more difficult. And obviously then you would have to take on, on your own, that's conveyed to you in a classroom. Like they couldn't take them.

[00:33:18] Dan Hudson: It you're absolutely. You're absolutely right. Those are the things that are incredibly hard to teach. They're also incredibly hard to measure and to certify against. Right. And so you don't see credentials out there so much for these types of skills that are so important, right? Yeah, absolutely.

[00:33:41] Ted Hallum: There's no certificate that says you're a good communicator, but we sure know it when we hear one, right.

When someone gets up to give a presentation at a conference within the first few minutes, you know, whether the person is a good communicator, whether or not. But you do have to have some technical skills. So when we talk about up-skilling what do you think for somebody who's transitioning out of the military, or maybe they've been out of the military for a while, but they're just now encountering data science or machine learning or decision science or whatever the case may be.

What do you think is the most effective in terms of time, money? You know, all the considerations that people have to take off, which of course the GI bill helps with that, but what's the most effective path towards upskilling now, because there are so many different ways that people can go about it.

[00:34:28] Dan Hudson: Oh, there there's tremendous opportunity out there.

I mean it's wonderful. When you look at what is out there today compared to what used to exist when, you know, I was, let's just, it's so different today. So many barriers have been removed. So many resources are available at your fingertips through the internet. In many cases at no cost or low cost, it's opened it up to so many people.

The answer to your question is, geez, I guess it's like many things in life which may not be a satisfactory answer, but it ultimately depends. You know, it depends on, on where you are your background experience, where you're coming from, you highlighted some important factors there that the time, the cost, all of those factor into this I, I haven't seen in my position and in my experience that there's one path that is going to lead to success.

I've seen people take winding paths like me. I know where you've come from. I've listened to all your podcasts. Like many others out there, we've taken these winding paths to get to where we are today. So there's no single recipe for success. I guess at the end of the day, it requires a bit of introspection into understanding your own strengths and limitations, where the gaps exists and what is needed to get you from where you are today, to where you want to be.

I think mentors and the data science community, that we're a part of play, a critical role in that. In terms of helping inform your decisions about ultimately where you want to be, you know, a year from now two years from now, five years from now, 10 years from now, and that can help guide your decision about which pathway you want to take.

You know I chose to pursue a PhD. It's not for everybody. You know, ultimately that's at its core, that is a research research degree. Right? It's all about taking a deep dive into a very specific question that can advance the state of knowledge in that area. And that's important for some roles it's not important for others.

So it ultimately depends on what you want to do. And in the role that I served in today, you know, I'm, I'm looking for people across the spectrum, right. It depends. Whether I'm looking to fill a role on a specific contract where the requirements they're constraints are very well-defined in that case, I'm going to be looking for individuals that check the box on all of those specific things.

If you don't check the box, I'm sorry, but you're not going to fail. You're not going to be able to fill that role. In other cases, where we have an innovation lab, a guide house, for example, where we're looking to develop. There's a bit more of an opportunity to bring people from different backgrounds and experiences where diversity of thought and background and experience and skills are gonna get going to be so important for making sure that we develop an innovative solution that is going to have impact.

I want to make sure that that team is comprised of people that come from many different places and are going to bring different perspectives and skills to the team. And so, you know, you might be able to fill a role like that, even if you don't have a PhD or a master's, or you're not quantitative in nature.

I might want a social scientist, somebody who is focused on human centered design or design thinking, I want to have that person on my team. So. It's, that's a really tough question to answer, and I feel like I probably didn't answer it in the end. But those are some of my thoughts on that topic. Sure, sure.

[00:38:53] Ted Hallum: Well, I know that when, you know, as a hiring manager resumes come in front of you and you have to evaluate them, and like you said, many times it's going to be just the requirements of the position. And if somebody doesn't tick a critical box, then they're not going to be applicable for that role. But one of the things that I think is so cool about being able to have you on the podcast, our listeners being able to hear from you is what I looked over your credentials.

They you've, you've done everything from one of the data camp, career tracks to the industry certified analytics professional, the cap certificate to, like you said, the PhD. So you have hands-on tangible experience with so many of these different learning paths that people have as options. When you just generally look at a resume, do MOOCs carry much weight with you or industry certifications like the cap or formal education?

Just so when people think, man, where should I invest my time for the general hiring manager, you know, as it's perceived through the eyes of a hiring manager which of those are really worth it and which ones are duds, do you think?

[00:40:04] Dan Hudson: Yeah. Another great question. So I guess I'll, I'll share my thoughts as an individual and then I'll share my thoughts on what, what I think are the bigger picture issues on this topic as an individual.

I don't pay too much attention to where individuals went to school. I will say certain certifications grabbed my attention and, you know, I'll raise my hand and say that I'm, I'm biased in this respect because you highlighted the certified analytics professional credential. It's one that I value because it's industry agnostic, it's vendor tool agnostic.

It's just, it demonstrates a broad ability to deal with. And I know that it, because of the rigor associated with it, if you have earned that certification, you have met certain benchmarks or milestones. And so. Yeah. Like all of us were limited, limited in time. To me that is an important signal or indicator that you have met certain requirements and I value that.

But at the end of the day I don't place a whole lot of weight on an individual's resume, their qualifications, their credentials. I want to sit down with that person. Face-to-face engage in conversation with them and try to tease out, you know, first of all some of those soft skills, their ability to think critically in real time, to be able to frame problems, to explore issues and see how they communicate about their way of dividing and conquering that problem to ultimately come to some solution.

Right. Those are really important attributes to me. And then. Thinking about the bigger team. I want to understand where do they fit in the, in the bigger picture, right? Are they, are they going to be a force multiplier? Are they going to be a value added addition to the team that I'm building? So those are the things that I look at as an individual, but I mentioned that I want to take a step back and also think about the realities of where we are today.

I think realistically we're still in a place where the institutions that you obtain your degrees from the degrees that you have, the credentials that you have, those things are still very important. I think the data science community is trying to move toward a model where you focus more on a show.

Me don't tell me. Type of model where people are building out their portfolios, you can go to somebody, get hub and, and see the body of work that they have put together. And that works well for some scenarios and not others. You know, if you're working in a cleared environment, if the vast majority of the work that you do is, you know, behind the wall, so to speak and you can't share it publicly, those individuals are, are going to have to use some other mechanisms for demonstrating the kinds of value that they've added through the work that they've done.

But yeah, I think, I think we're still living in a world where the credentials that you have still carry a tremendous amount of weight.

[00:43:51] Ted Hallum: I love that answer because. It gets to the heart of, you know, what you did speak to what is, and isn't valuable about different credentials, but then also beyond that, what people need to bring to the interview, what people need to bring to their resume, to communicate what they can actually do and how to show what it is that they've done in the past, in a tangible way.

Because that is going to be a key differentiator. Between a lot of candidates who get jobs in those who continue to look for jobs. Now kind of as a segue question from the skills topic to more of, kind of the future of data science and machine learning and analytics if you to kind of riff off the idea of a killer app, if you had to identify one killer skill, when you look out strategically over the next five to 10 years, that you think, man, if people have this, they're going to get the best return on investment for their time and effort to, to obtain XYZ skill.

What would that skill be within our, our field?

[00:44:57] Dan Hudson: Oh, it's a great question. I'm going to go back to some of the skills that I highlighted before. I, I really think that it's going to be more on the softer side than on the hard side. I think our field is evolving toward increasing use of automation where it makes sense. And so there are various elements of the data science and machine learning life cycle, where we're increasingly depending upon automation algorithms, to be able to.

Address those elements of, of the workflow. And it's not any different from the field of robotic process automation and intelligent automation at large, it's all about, you know, where there are certain process elements that machines are better equipped to do something. Then we, as humans are, we're going to automate those things away.

And I'll go back to that quote that I talked about earlier in, in referring to soft skills where Cassie causer Krav says, you know, I don't like to label them soft skills. It's those skills that are difficult to automate. And so I think. I, I don't know if this is a controversial viewpoint or not. But as we look to the future, I think those individuals that focus more on honing those softer skills and in particular, I, I think there is tremendous opportunity in the field of design thinking and human centered design within data science.

I think that is an area where there's going to be tremendous return on investment five to 10 years from now,

[00:46:52] Ted Hallum: for folks who might not have read on it or B be informed on the topic, human centered design. Could you kind of elaborate on exactly what that is real quick?

[00:47:01] Dan Hudson: Well, yeah, I mean it fundamentally, you know in the data science space data in and of itself, doesn't have a whole lot of intrinsic value, right.

Tight step, we can extract from that data and use those insights to help make better decisions. And so, you know, if you think about the logical flow, it goes from data to insights to a decision. And then beyond that, once a decision is made, there's extra work that needs to be done, right? It's taking action and helping an organization navigate the change that comes along with those actions that are taken to ultimately realize value at the end of it.

Right. And so that, that bit about going from insight to decision from decision to action and from action to realizing value. All of that fundamentally involves people. They're at the center of this. There are consumers of the insights that are generated from the work that we do. There are people involved throughout the process.

And so human centered design is fundamentally about taking that perspective of the consumer of the work that we're doing to help shape the way that we do things that the way that we construct the analysis, the way that we can communicate about it all along the way there are people at the, at the heart of it.

And so that's what I think is gonna be really impactful here. Moving forward. That's

[00:48:44] Ted Hallum: fascinating. Thank you for unpacking that for us. So I want to take an opportunity here to, to tap into the strategic role that you're in and the perspectives that you have to take on in that role. I know that you're probably thinking a lot about where, because data science and machine are moving so fast.

I think in years who knows what it's going to look like, I think it's going to be radically different. But when we, when we look back, it's like 2011, when data science really kicked off kind of in the, in the public consciousness with deep learning, coming into its own with the power of GPU's and all that stuff.

I think it's been it's been two or three years. It's pretty neat. And then it began to grow and now, you know, I think it's, it's something that Yeah, we've gotten to the point where one person that came onto this show said, every company is a data company. Some of them just don't know it yet.

So that, that's kind of where we are at this point. Now a few years ago, I think it probably made a lot of sense to be a data science generalist, because you would get an array of skills that went from statistics to some decision science and maybe building some models. And then that made you generally applicable for the vast majority of roles that were out there.

You, you could kind of go out to the job market fish with dynamite. But you know, as thing as things have matured, I get the sense that that's probably changing that maybe being a generalist is not the best strategy when you're upskilling and getting ready for the job market. But you know, I'm not in a hiring manager role.

So I'd love to throw that at you and see, you know, what do you think about data science, generalists? And do you think that that specializing is maybe a better strategy?

[00:50:32] Dan Hudson: Yeah, I think that much like my previous response to one of your questions, I think it ultimately depends and requires a bit of introspection in terms of where you ultimately see yourself landing the kind of environment that you want to work in.

You know, even today you talk about every company being a data company, but. There are many, many small businesses out there. And they continue to grow day by day. If you're, if you want to work in a small business environment they're often looking for individuals to come in, stand up their data science practice.

And if you're going to serve in that kind of role, I think being a generalist is going to be really helpful for you. On the flip side of that coin, if you're going into a large enterprise Oftentimes, those kinds of enterprises are looking for individuals that have niche expertise, whether it's in data engineering data analytics, machine learning decision science they're often much more focused in the kinds of roles that they're looking for.

So I don't think there's a single right answer for this. It's, it's sort of about figuring out, you know, the kind of environment that you work in and I can tell you I've my, my journey has taken me down a couple of different paths here. When I left the nuclear regulatory commission, you know, at that point I had.

Served for the better part of my adult life in support of the federal government, whether it was on active duty or as a civilian federal employee. My first venture into the private sector was in the small business environment which I really loved. There's a lot of value that comes from serving in that kind of environment.

The cultural aspects are very much aligned with what we're accustomed to in serving in the military. Being part of a unit everybody's closed. It's small people are working hard, wearing multiple hats in support of the mission. And I valued that experience tremendously. And now I find myself in a large business environment and I see the benefits of being there as well in terms of the, the resources that are at your disposal, the diversity of the team that you have, that you're working with.

Lots of different benefits that come from that too. So it's, it's sort of figuring out, you know, at least in the early stages, what you think is going to work best for you. In some cases it might be being a generalist. In other cases it might be hyper specializing in a particular area. And obviously it has to align in both cases with.

Cite you, you know, you know, when you wake up day in and day out what it is that you want to be if you want to tackle a diversity of issues, maybe being a generalist is a better path for you. If you want to be hyper-focused and have maybe better defined boundaries around the types of problems and issues that you confront being a specialist might be the right path for you.

I don't think there's a right or wrong answer here. There's plenty of opportunity out there for both. So you just have to sort of navigate that process for yourself. And again, I'll go back to a comment that I made earlier, engaging with the community, finding mentors for you, I think can help you tease those things apart.

If you're, if you haven't been there before and you and your, you don't have a good enough information base to, to inform those decisions that you have to make sure.

[00:54:24] Ted Hallum: Well, real quick, since you mentioned mentorship, I'll plug. If you're listening to this episode and you'd like to get them into her, to just help you navigate the world of up-skilling or finding a job, navigating industry, any of that kind of thing, head out to vets in data science.com, and you'll be able to get to the navigation bar, go to mentorship and you can request a mentor there.

All right. Having said that. Dan, one of the things I wanted to drill down into, you mentioned that specializing probably makes more sense if you're in a larger organization. And then at one point you used the term hyper specialized. So I want to drill down into that a little bit. If people are listening to this and they want to go to a large organization, do you think it's best to think about specializing in terms of like the granularity of decision science versus data engineering versus analytics versus machine learning or at this point in time, should people be thinking about hyper hyper-specialized specializing more.

Computer vision scientists versus NLP scientists versus acoustic modeling engineer or something like that. Like what level of specialization is it appropriate in 2021?

[00:55:35] Dan Hudson: Yeah. Great question. I think there's a spectrum. I think there's a broader domain. You know, if you look at data science, there's data science as a whole within data science, there might be these subspecialties, like I talked about before data engineering, data analytics.

Statistics, machine learning, decision science, there might be those subspecialties. And then when you go a little bit deeper down, you know, within the field of machine learning, there might be deep learning. There might be specific application areas like computer vision, natural language processing, all of that.

And then I think maybe even at the lowest level, there might be specific technologies that you specialize in. You might be an expert in leveraging this particular technology. And there are opportunities out there for individuals who have that kind of expertise. You asked about certifications before there are absolutely.

This particular certification on this particular platform and you qualify for that. And, and so that's available to you. So I think when I use the term hyper-specialization hyper specialization, it's, it's more so thinking about that, where you've sort of worked your way down the tree there's subspecialty, there's maybe a particular application area.

And now we're even down to the area of specific tools that in my view is where you're hyper specialized, where it's down to the specific platform or technology that you're working with, and that there are plenty of opportunities that exist for those, those people.

[00:57:13] Ted Hallum: Now I had one burning question that I wanted to ask earlier, but I held off till this point, because I knew that we were going to talk about more of the options of our career field and whether people should be generalist or specialize in one of these areas.

Now that we've talked about all that earlier, you talked about the value of the certified analytics professional certificate and how that's a technology agnostic industry certification. That's very rigorous. So first it's kind of a two-part question, but first I'm curious for our listeners benefit, what does that entail to get that certification?

[00:57:49] Dan Hudson: Oh, yeah. So the certified, the analytics professional certification is, is managed by the it's called informs the Institute for oper operations research and the management sciences. The pro the certification program has been around I guess probably for around eight years at this point.

So you talk about the birth of data science being as a term, at least around the 2011 era. So the certification program was instituted probably a couple of years after that. I was an early adopter of it because I, I saw the value and potential for, for that field. I earned my certification back in 2014.

So what I love about the program is that. It takes a holistic view of the analytics and data science life cycle. They worked with practitioners in industry and government and academia to break down using a formal job task analysis to understand what it takes to be successful, to, to work in this space.

And so. They constructed requirements around your experience around your technical knowledge. And so there, there is an exam that you have to take and pass to be able to become certified. But then you've heard me time and time again, in this conversation emphasize the importance of soft skills.

And that is an element of the certification as well, which is something that I think is unique for this credential because for many other credentials out there, it's, it's all about the techniques and technologies. You understand how those work, you take an exam to that, that. To apply those techniques and technologies.

This incorporates the skills as well. You have to have customers that you've worked with before, that will attest to your ability to deal with, you know, problem framing, critical thinking, communications, all of those different aspects. And so I really value the certifi certification from that perspective as well.

[01:00:09] Ted Hallum: So I definitely wanted to, to kind of tease out more about this one, because I could tell from your answer earlier that this is one, when you see it on a resume, it carries a bit of weight. Obviously you're going to want to talk to that person and, and find out more about their personal problem solving approaches and all that.

But just seeing that on a resume, I could tell that that that would resonate with, and maybe, you know, influence at least willing to have conversation with that person. My last question about the cap certification is, you know, we've talked about how people could do anything from data engineering and analytics, all the way to some hyper specialized, like a certain technology or computer vision scientist.

You know, the breadth of data sciences is vast. So for the certified analytics professional, is that something that's sort of universally applicable to everyone and that whole data science landscape, or would you recommend that certification for, for people in certain flavors in data science?

[01:01:09] Dan Hudson: Ah, that's, that's a great question.

I, so when I talk about the broader umbrella data, science said, I've even included within that data engineers, right? And so I think the certified analytics professional credential wouldn't be a good fit for somebody who wants to specialize in data engineering. But for those individuals that are part of the.

Entire life cycle, you know, from engaging with a client, a customer, or it's internal to your business, you know, where are you? Starting to think about a problem and how to define it and how to convert that business problem into an analytics or data problem, and then to bring the data to bear and to build models and ultimately get to that place where you're extracting insights.

And in some cases, you're some cases you're deploying a model into production. In some cases you're producing a final report that includes your findings, your insights, conclusions, and recommendations, whatever the case may be. If you play a role in that process. I think being a certified analytics professional, especially, especially if you are a leader, if you're managing that process from end to end, I think that credential is incredibly valid.

[01:02:37] Ted Hallum: Awesome. I think that th there are a lot of people who have heard of that certification. They don't really know if it's spot on applicable to them, or if it's, you know, less so. And I think you really helped to kind of wrap the context around that and that people would need to make that decision about that certification.

I appreciate that. There at the end. I heard you mentioned getting models into production, and that makes me think of one last skill related question that I'll throw at you. And that is, I think for a long time data scientists generally live in the world like science fair projects and building proofs of concept, just to show that they could get something really complicated to work.

I mean, it was, it was valid because they were, they were doing groundbreaking things that no one had done before and having to prove to their organizations that they could do it. At this point in time, I th I think a lot more organizations are they've they've, they've seen the proof, they know that something can be done now.

They want it to be in production where it can actually. Yield business value. So how much do you think people getting into this field need to focus on things like software engineering as it gotten to the point where getting true? Not just, not just the ability to code, but getting true software engineering skills.

Is that like a requirement at this point? Or only for certain people, do you think?

[01:03:57] Dan Hudson: Yeah, I think ultimately it's, it's gone again. I guess this goes back to a point that I was making earlier. It kind of depends on the environment that you're going to work in. If you're going to be in that small business environment where a lot of people are going to be looking to you to essentially cover the entire spectrum, then having that understanding that the software engineering skills the DevSecOps.

You know, ML ops, whatever, whatever label we want to attach to it, depending on the space that you're working in that, that critical piece of getting from, you know, a developed model to something that is in production. If you're in that small business environment, you probably need to have that be a an important part of your toolkit that you can point to in a larger business environment where you're going to have a larger team than you could work with.

It's probably not quite as important though. It is important to have awareness of of these concepts and where you fit in and, and touch those and interact with them. So I guess that that's, that's ultimately what I would say is that it kind of depends on, on the team environment that you're going to be working within the level to which you need to understand these things.

But I think it's fair to say that those areas are becoming. They're, they're recognized as becoming increasingly important as you transitioned from the development phase to the opera operationalization phase. And, you know, I I've listened to your podcasts. I believe it was Rob from Octo that you worked with before, who highlighted this area as, as an important need, you know, he, he, he talked about, he talked about the fact that it's people, the science products as these widgets, and instead they need to be treated as these living entities that need to be cared for and maintained.

And I wholeheartedly support that point of view.

[01:06:06] Ted Hallum: That's awesome, man. Well, Dan, this has been a fantastic conversation. All, all the hard questions are behind us. I want to wrap up with a couple of softballs, just because folks like yourself, you have a brochure appetite for knowledge, and oftentimes you have a pulse on some of the best resources. So what are your top recommendations for data-related podcast?

[01:06:34] Dan Hudson: Oh, this is you. You said this was a softball question, but that's actually really hard because there are so many out there that I would like to point to. I guess I would I guess what I'll go with here is some, some selections that I think may not be obvious and that others that are in the data science community might point to, and I'll go back to what I said earlier about data not having intrinsic value in and of itself.

And ultimately what we aim to do with data is to utilize those data, to help make better decisions. Decisions I think are at the core of what we do in the data science community. And so I'll point to some resources that I think are really helpful in the decision science space from a podcast perspective, I really like choice ology.

I, I think it was originally Led by Dan Heath. And now it's Katy milkman from the the Wharton school at the university of Pennsylvania. But what I love about that podcast is that it highlights the many types of biases that creep into the kinds of decisions that we make and how to guard against them to help make better choices.

And I think that's really appropriate in the data science space because across the entire life cycle we have to make a number of decisions along the way, you know, whether it's how to frame a problem, the assumptions that we make the features that we want to include in a model, there are many, many types of decisions that need to be made throughout the life cycle.

And so I think. From a data science perspective. And as I think about the audience, listening to this, you know, fundamentally people who are curious about how to break into the data science space, they're at this inflection point in their careers and they have some tough decisions to make. So I think having some resources that there then better equipped to make these tough decisions along the way.

I'll I'll point to some of these. So choice ology as a podcast is one that I'd highlight. For books. I, Annie duke released a book last year called how to decide. And for those of you that don't know, Annie duke, she's a former professional poker player won millions of dollars as a poker player.

And, and she has put out a couple of books on decision-making that I think will be really useful for a broad spectrum of decisions that need to be made, whether they're in life or in business. So I'd highly recommend how to decide her. Her earlier book was called thinking in bets. And both of those are incredibly useful because the reality is in the data science space, we're often making predictions.

There's uncertainty that comes along with those predictions and having. Tools and techniques at your disposal for how to grapple with that. Uncertainty, I think is going to be incredibly important for you.

[01:10:05] Ted Hallum: Yeah. Those sound like phenomenal resources. I can't wait to check them out myself for anybody that wants to give that podcast choice allergy a listen, or the book that he recommended links to.

Both of those will be in the show notes where you can tune into the podcast or pick up a copy of the book. The last one, Dan, super easy. So I've got your LinkedIn username here on the screen. It's been on throughout the duration of the podcast. Is that your preferred way for people to reach out to

[01:10:32] Dan Hudson: Yeah, LinkedIn is great. I'll be completely honest with you. Unfortunately I think many of us get too many. Met messages that are directed at us on LinkedIn. And so oftentimes I, it takes me a while to get through those and, and to respond to people. So for, for those of you that are listening and want to, to really engage with me and connect and have a conversation you can reach out to me at I'll give you my personal email that that I check regularly.

And that is Dan Hudson phd@gmail.com.

[01:11:15] Ted Hallum: Awesome. Dan, it's been my pleasure to have you here on the data canteen. I think this has been just, my mind is blown. The conversation has been awesome. I know it's going to be of tremendous value to our listeners and I look forward to having you again on the future.

I hope you'll come back and

[01:11:30] Dan Hudson: join us. Absolutely. Thank you so much for inviting me to be a part of this. And honestly, I can't imagine a better population of people to serve the work that you're doing. I know this above and beyond the work that you do in your professional life. So thank you for for hosting this and for providing a mechanism for helping individuals who have gone down the path that you've gone and, and trying to make it easier for them.

So really appreciate that as well.

[01:12:01] Ted Hallum: I have a great time with it. I love engaging with our community and we look forward to talking to you again in the future.

[01:12:08] Dan Hudson: All right. Fantastic. Thank you.

[01:12:12]Ted Hallum: Join you on this conversation with Dr. Dan Hudson as always until the next episode, I bet you clean data, low P values and Godspeed on your data journey.