Ocean Engineering to Data Science

Listen

Transcript

[00:10] Tim: Welcome to episode 7 of the Into the Hopper podcast. I’m joined today by my friend and twice former colleague, Brenton Mallen. Brenton studied ocean engineering and worked as a systems engineer before making a pivot around 2015 into the data science field. Brenton and I worked together at a cybersecurity startup for several years back then, and then recently worked together at my current employer until last week, when Brenton moved on to a new job. In fact, I guess around this time last week we would have been having our one-on-one because you were my manager for a brief period of time.

[00:49] Brenton: Yeah, that feels like a lifetime ago already.

[00:57] Tim: It’s because you haven’t been working all week, unlike me.

[01:02] Brenton: Fun employment definitely has its benefits.

[01:05] Tim: We can just consider this our one-on-one for the world to hear.

[01:10] Brenton: Okay, I’m down for that.

[01:12] Tim: All 150 people in the world anyway who listen to this podcast.

[01:16] Brenton: Seems optimistic.

[01:18] Tim: So what’s ocean engineering?

[01:23] Brenton: Good question, and I get it a lot. Ocean engineering is like all other engineering disciplines, focusing on solving problems. Lots of math, lots of physics, all that stuff. It’s essentially an amalgamation of multiple engineering disciplines in the marine environment. So you study things like corrosion, wave mechanics, and things you would think of when you think of marine classes, like oceanography. You also take things like computer classes, mechanical classes, all kinds of stuff, which I think is what appealed to me. I went into school as an EE, or electrical engineering, major and switched to ocean engineering mainly because I didn’t know what I wanted to do. Ocean engineering had a very broad program that allowed me to get exposure to a bunch of different topics so that I could maybe find something that was more of my calling.

[02:30] Tim: That’s interesting. My master’s degree is in operations research from North Carolina State University, and our program was similar in that it was an interdisciplinary program. And I really enjoyed that aspect of it. I was able to take classes in computer science, industrial engineering, math, statistics. People could also do like business, engineering, economics, things like that. But it gave me a breadth that’s been really valuable in my career as a data scientist.

[03:05] Brenton: Yeah, I think it’s about perspective, right? If you broaden your perspective, you can appreciate a bunch of different things and approach challenges in a different way than somebody who hasn’t had that perspective.

[03:18] Tim: So what kind of things do ocean engineers do in industry? Like, they build buoys?

[03:24] Brenton: Uh, yeah, certain buoys. Um, so the main 2 destinations I think most of my classmates have probably gone into is either going to be, uh, naval defense work or oil industry. I haven’t really kept up with anybody to know if they’ve gone elsewhere, but typically your naval will be things like— I was working on, part of my master’s was working on underwater mine countermeasure stuff. So, you know, underwater vehicles, that sort of thing. Oil could be anything, you know, oil rig stuff, I think. I haven’t— I didn’t want to go into it, so I didn’t really look into it much. And I fell into the defense world mainly because of my master’s thesis was funded by the Navy to do underwater acoustics processing, digital image and signal processing.

[04:21] Tim: Do ocean engineers build ships or is that some other engineering discipline?

[04:25] Brenton: That’s a good question. Naval architecture is something you can do that we took classes in that as well. You could go into ship design. Um, I think an old colleague went to Newport News, I think, to build some— to do some naval architecture stuff. Um, you could also do underwater vehicles, autonomous vehicles. That’s a thing people do. Uh, there could be— I have a— had a friend that went into, I think, something to do with bridges and corrosion of bridges, um, around, you know, saltwater and stuff like that to improve integrity. So you can do a lot of things. I think it really, it’s all around being around the marine environment really. So it can be very harsh on different things like your materials and all that stuff.

[05:13] Tim: You worked in that broader space for several years and then got interested in data science. I’m interested in having people hear about that transition because, with data science, everyone kind of has to tread their own path. I think you took an interesting route there. It’d be helpful for people to hear about it.

[05:37] Brenton: Yeah, I think so. It didn’t start somewhat early on where I got some exposure. I took a data mining is what they called it back then. I guess maybe some people still call it, but it was a data mining course I took in grad school. while I was doing my project. My project was essentially called Target Discrimination Using Low Frequency Sonar. So essentially, you can have an object laying on the ocean floor or beneath the ocean floor, and if you use a low enough frequency sound wave, it essentially induces a mechanical load onto the object and therefore causes it to ring, kind of like you would hit a bell and it would ring, that ring would be parametric to materials and shape and size of that object. So trying to find ways to discriminate between types of objects in that way was really my focus.

So essentially, if I were to call it in machine learning terms now, it would be feature engineering, trying to come up with ways to figure out how to discriminate between objects of different types. I started out with actual CFD, so computational fluid dynamics modeling, to try and understand really the physics going on with that vibration in the objects. So it wasn’t really particularly out of the blue, but I didn’t really start getting into it until my second job in the defense world where I met our mutual friend William, and we got into doing Kaggle competitions, or at least I got introduced into doing that. One of the first Kaggle competitions we did was Galaxy Classification.

And I think I didn’t do any of the machine learning part of that because I was still pretty new, but I was able to do the image processing part of that because of just past experience. So I was able to do some image segmentation for the galaxy pictures to remove all the background stars and all the noise essentially in the image to isolate the galaxy of interest. And then I think William and maybe a couple other people were involved doing the actual modeling. And then from there, I taught myself Python and all the machine learning stuff that I’ve done.

[07:58] Tim: Had you been doing programming in your schooling? I guess MATLAB probably?

[08:06] Brenton: Yeah. So we started off, I took one course. It was C++ and MATLAB joined. And then everything else after that in class, the homeworks and all that were done in MATLAB. Mainly, I think the visualization, MATLAB is pretty great for that. It’s not great on the wallet though.

[08:23] Tim: What was your route for learning Python?

[08:26] Brenton: Just through that Kaggle competition. I wasn’t really doing much at work. Funding, at least for the defense world, was pretty slim during the time, and we were pretty low in terms of tasking. So I took the time to teach myself Python. It’s really approachable, which made it easy. Especially with NumPy, it’s very close to something like MATLAB, where you’re dealing with arrays, matrices, and all that stuff. It was an easy transition.

[09:03] Tim: I had not done a lot of MATLAB before learning Python, so I didn’t have that familiarity. But I think, I mean, that’s certainly where the NumPy and that kind of world came from was former MATLAB people. So that makes a lot of sense.

[09:20] Brenton: And you’ve done Python predominantly now?

[09:25] Tim: Yeah, it’s the only programming language I use.

[09:27] Tim: As you were learning, you did this Kaggle competition. You were able to then transition into data science through— well, I guess our mutual friend and former colleague took a data science role at a tech or cybersecurity startup, and you were then able to make a move onto his team. Is that how that worked?

[09:51] Brenton: Yeah, I rode those coattails and it wouldn’t be the first time that I’ve done that, I guess. So, I was fortunate enough to apply for a job. I think they had sent an offer to someone else and that person declined and I guess they were left with me. So, they took me in. It was a very small data science group for research and development. in cybersecurity for bot mitigation. And pretty soon after I’d started, it became just 2 of us. And, you know, we had to— we were responsible for everything, building everything, monitoring, all that stuff, doing just research in general. It was a great time to learn a lot. I learned a lot very quickly, sort of trial by fire kind of thing.

[10:41] Tim: That was your first introduction to like AWS and that world, or had you done any of that before?

[10:48] Brenton: I had not done any of that before. That was my first time doing AWS, uh, waiting for EC2 instances to provision and all that good stuff.

[10:56] Tim: Here you are 6 years later, still waiting for machines to provision.

[11:01] Brenton: I’ve gone serverless mostly, so I don’t have to wait for anything.

[11:07] Tim: People ask me routinely, like, how do I move from math into data science or from something into data science? Like, do you have from your own experience or whether you recommend your own path or just like things you’ve learned, advice that you give to people trying to make that move?

[11:27] Brenton: It’s really hard to say. It’s a different world, I think, now than even when I started getting involved. I feel like competition’s gotten way tougher than it used to be. I feel like sometimes there are jobs that like, if I were to start applying now, I wouldn’t be able to actually get, you know, which is where networking helps. So, I guess number one is networking is very beneficial. Also, Having a portfolio, I think, helps a lot. Being able to be in a position to demonstrate your skill set in some way is really good. I’ve had people express that they were appreciative that I had a portfolio that they can go look at and see what my abilities and skill sets are.

[12:18] Tim: Can you elaborate on that? Like, what does a portfolio look like or what can it look like?

[12:25] Brenton: I mean, it can look like a bunch of different things. I can just talk about really what mine is just because that’s the only experience I really have. And so mine is essentially a website with some blog posts. I think my cadence right now is like one post a year, which isn’t really helpful. But it is something that is there. If it’s written down, it’s recorded and you can point to it. It’s tangible for somebody to gain an appreciation for what you can bring to the table. It could be blog posts about anything that you explored or learned. It could be web applications that you’ve made and are accessible to somebody to give a try. For something like machine learning, it’s really difficult because it’s just a very broad definition of what that job role can be.

It can be anything from more of an ops MLE type of person to just a research and development data exploration kind of role where you don’t really write production software or something like that. You write exploratory code and then someone takes it from there kind of thing. So it really depends on where in that spectrum you feel like you want to lie. There are benefits to being specialized in one area and there are benefits to being more of a generalist, which I think is where I sit in that spectrum. So in terms of portfolio, you could demonstrate your ability to go quote unquote like full-stack machine learning where you can have a model in production somewhere sitting that someone can access through a web app.

And you can demonstrate, hey, I built this model, this is what I did to build it, these are the things that I encountered while trying to do that. But you can go ahead and play around with it here. And you can do that pretty cheaply and quickly using something like serverless infrastructure, something like Zappa or something like that to deploy. a web API. So I have a couple of those on my portfolio. They’re old and I haven’t done anything new, but it is still there. Like I said, it’s tangible for someone to appreciate what you can do, which is really important in my opinion.

[14:35] Tim: It seems to me, having interviewed a lot over the last 10 years, that there’s kind of 2 phases. One is like getting your foot in the door to actually get the interview, which networking helps a ton with. Like a lot of places, if you can get an internal referral, you can get connected with a recruiter at least. And also, I think that maybe often where your portfolio comes in, like, you know, it’s something that makes your resume stand apart if somebody comes across your website or something. And then after that, it’s the interview process, which in my experience is often fairly detached from that first stage. Sometimes the portfolio type stuff comes up in the interview, but in my experience, it seems like people are more interested in, yeah, I mean, kind of the more traditional like interview questions or a take-home problem or something. Which are— so those 2 phases are somewhat different skill sets, but you really have to work them both. Like you could be awesome at live coding problems, but if you can’t actually get an interview, then you don’t get the opportunity to show that or vice versa.

[15:53] Brenton: Yeah, I definitely agree. Portfolio stuff is helpful for getting you in the door and across the threshold, but interviewing is a skill set in and of itself, and it’s an art form, I guess. You have to practice, and it’s tough, right? Because nobody interviews the same. Sometimes you’ll go to a place where they’re really technical, and sometimes you’ll go to a place where they’re more— someone once used the phrase “will versus skill,” where they’re more interested in your tenacity than they are in your skill set. So it’s really hard to prepare generally for that, because everyone does it differently.

[16:36] Tim: I have a blog post I wrote several years ago now called Reflections on Being Turned Down from Data Science Interviews or something like that, where I kind of outlined a lot of my experience with that very problem. Companies are actually getting better at providing more kind of guidelines as to what gonna cover in the interview. But, you know, a lot of stuff could totally come out of left field. Even if you do like live coding type things, which are pretty common, like you don’t know. You know, I had an interview where I was asked to— it was actually a problem that was copied straight from the LeetCode website, but it was like reconstructing a binary tree from an infix and prefix representation of the tree. And it’s been like, I don’t know if I’ve really thought about the terms infix and prefix since taking algorithms in what, 2011 or something like that. It’s just like, it’s not, it’s like not an insanely hard problem, but just if you’re not something you’re familiar with, it’s kind of a tough one to tackle, especially if you don’t remember what the terms mean, which is where I was.

[17:49] Brenton: Yeah. And that if it’s a surprise or you’re not really sure of what to expect, I mean, it compounds your emotions through the interview process where you can stumble and you get even more nervous and things like that. And then you start getting self-conscious of like, oh, these people are thinking that I don’t know what I’m doing and all that stuff, which is a big part of all that, at least for me.

[18:12] Tim: I don’t know if you want to share about— you had a little bit of experience in doing a Data science mentoring program.

[18:19] Brenton: So I did a mentorship with a mentee through SharpestMinds. He has actually successfully landed a data-oriented role, which is— it’s humbling, right? You know, a goal of mine is to just try and have a positive impact on the people around me and being able to do that and see it realized in that way is very rewarding. But yeah, so my goal through that was to try and provide as much perspective as possible. So the curriculum I set forth in that was to go from looking for a dataset, something that is of interest, asking questions that you want to find answers to, You know, munging around with the data, figuring out the challenges there, processing it to a standpoint, and, you know, building a model.

And things that come along that process are things like, okay, well, you realize really quickly rabbit holes can get very steep very quickly, and you wind up falling them, like, down them. And you have to realize that you have to narrow scope to a point so that way you can make sure you have something tangible at the end of it. The mentorship having been time bounded, you know, helped a lot with making sure that we did that. But, you know, it’s just something you have to be cognizant of. So the goal for me was to make sure that they got as much perspective as possible around the entire process, not so much to train them on being able to do all those things, but to get exposure to maybe highlight areas that might be of more interest than others.

Like we were talking about earlier, where it’s just machine learning is a broad spectrum of responsibilities or roles, and you may want to be a part of all of them, or maybe some of them just don’t really interest you at all. So trying to provide that perspective of each of those processes along the way was really my goal. It’s interesting to— It’s sort of where I got the idea, like the perspective that trying to find a data science job now is very much more difficult than it used to be, at least in my experience. The expectation of what you can do is, or what you should be able to do when applying for a role seems to be pretty high. They want everybody to go from doing Python coding to actual production system level development and management and all that stuff, um, all of your cloud software or architectures.

It’s just there’s a lot that people expect, and it can be pretty overwhelming, uh, to try and, and present to somebody that you have experience and all that sort of thing. Um, you know, and I think if you can— I may have gotten off track from the question, but I think if you can illustrate in some way or demonstrate in some way that you have a working knowledge of those things, I think that can go a long way, even if it’s not something that’s an expert— you’re like an expert in. Um, so yeah, lessons learned. Let’s get back to the question, I guess.

So lessons learned from the mentorship was that, right, is trying to make sure, realizing that the expectations of the role are so high, and trying to figure out a way to, in a limited time, expose somebody to each of those areas just made me realize how much there is and how overwhelming it can be for someone new.

[22:07] Tim: Yeah, it’s a tough world. I mean, I don’t know often how to respond to people who want to move into the field. It just feels very complicated and it makes me grateful that I started in the early days, I guess. And not that there isn’t opportunity and need for people, but I think it’s still such a fuzzy and amorphous field of data science or machine learning that it’s It’s tricky for people to get in. Although, I mean, at the same time, I guess there’s more being taught to undergrads these days. I mean, when I was at North Carolina State for grad school, 2010 to 2012, even then, like anything machine learning, data science— well, NC State did kind of pioneer a data science master’s degree, but outside— and that was kind of a specialized program, but outside of that, in the computer science stats department, other departments.

Machine learning was kind of an oddball still. And I mean, I took a few classes, but they were pretty small and not popular, much less like the whole kind of world of just the technology and like Python, cloud infrastructure, all that kind of stuff. Maybe that was starting to come up in the computer science department, but I didn’t learn any of that in school for sure.

[23:44] Brenton: Yeah, it’s definitely become more common. I think probably one perspective that might be missing is the business side of it. You know, we focus on engineers trying to learn the trade and all that, but there’s also the business and the product aspect of where does machine learning fit in, you know, the business or the product that someone is trying to produce. Which can be just as important as the data scientist coming to the table.

[24:14] Tim: Or more important.

[24:17] Brenton: Or more important, right? Yeah, it’s one thing to bring a machine learning person on board to solve a problem. It’s another thing to bring them on board to find a problem to solve. What I’m trying to say, I guess, is that companies want to employ machine learning engineers or data scientists to do certain tasks, but there hasn’t really been in terms of formal, at least not that I’ve seen, formal discussion around how do we formulate a data science problem and what does that really mean and what are the benefits and the costs? of doing that or exploring that.

[25:03] Tim: Do you have personal experience with this conundrum?

[25:06] Brenton: I think you and I both do.

[25:08] Tim: Yes.

[25:13] Brenton: For sake of my future employment, I’ll leave it at that.

[25:17] Tim: Yeah, I think that’s a huge issue. I mean, I saw this for my very first job out of grad school. You know, I tell people I was a data science hype hire like that. I was the first person hired at this fairly large consulting company that had a lot of very technical people. I was the first person with the title of data scientist. And then when I was brought on, it was just like, oh, we’ve read articles about data science, but why do we actually need a data scientist?

[25:52] Brenton: Right.

[25:54] Tim: And yeah, it was unclear as to what I was there for. And that’s a hard thing, hard road to trot. And I’ve somewhat avoided that particular problem in recent years by moving more onto the engineering side and being more kind of engineering support versus the one being sent out to actually find problems to solve and figure out if the data can solve the problem. Because That is actually very, very hard.

[26:20] Brenton: Yeah, I guess in terms of like if you were a job seeker though, there are ways to try and snuff that out from a potential company you’re interviewing with or something like that. I guess one of the things I try and do is understand level of patience to explore ideas, what their expectations are in terms of what do they think machine learning can do for them. Try and understand whether or not the people asking something of the team appreciate what they’re asking. What are the challenges and what are the costs and the risks, essentially? Because like we were talking about rabbit holes earlier, you can go down a rabbit hole thinking one thing, find out some more information, and that leads you down another one. And you could be 5, 6 months in a project and really not have a— product out of it because you’re finding new information as you go along. Is there a tolerance for that at the company you’re working for kind of thing?

[27:19] Tim: Do you think that’s something you can like ask about at the interview phase?

[27:23] Brenton: Yeah, I was trying to think of like, maybe I should have posed the question of like, well, how would you do it if you were trying to suss out whether or not you are that DS hype hire, or are they serious about it, or are they making traction, or is this something completely new? If it’s new, can you articulate what the risks are there and use that as part of your negotiation, I guess, for the role?

[27:50] Tim: I’ve partially tried to mitigate that by not being kind of a solo data scientist, like going places that have established teams. where that doesn’t fall entirely on me.

[28:07] Brenton: Right.

[28:10] Tim: As well as like trying to understand what the role of product management is at a company and just asking questions about like, what’s— who sets the product direction? You know, where is it? What’s the direction right now? What’s the relationship between the data science team and product manager or management team. And even just trying to understand like where a data science team fits in the broader organization, I think is informative. And there are different models for that and I think different models can work. But I don’t know, I just try to probe as much as possible into those questions as I can. And I find that People, once you start asking questions like that, they start to resonate usually with the people you’re interviewing with. And even if you don’t get the total story, you can kind of start to read people’s reactions and hear what things give them nervous laughter about what’s going on in the company. Yeah.

[29:22] Brenton: I think that kind of segues into Another really key skill set, I guess, for the role would be communication. And I think that’s— and like going back to the mentorship, that’s another thing I tried to support and foster is communication. So that can be in different forms, right? It could be communicating your work in terms of presentation. It could be communicating to people in interviews. Setting expectations, all that kind of stuff. And that is a skill set like any other that takes practice. Um, it takes a certain level of confidence, I think, too, which is gained over time through experience.

Um, but it is pretty critical to be able to express what your intentions are, what the expectations are, understand and question other people’s expectations and perspectives, making sure that you’re all on the same page, you know, and doing that at some regular cadence so that you maintain that level of sort of shared understanding of what’s going on. In terms of interviewing, you know, being able to sell yourself, your skill set, that is something that’s always been extremely difficult for me. You know, it’s definitely not easy, but practicing and being able to be articulate and talk confidently about your experience is pretty major. And then that goes into the role as well, right?

Being able to talk to your stakeholders, whoever is going to be consuming your output— could be somebody technical, could be someone not technical— being able to do that is and express that and share those ideas so that people understand is a pretty key skill set to have. I mean, it could also even go to the networking, right? Like if you’re going to go to a conference or something like that, give a talk, being able to talk about your work eloquently, or at least maybe not eloquently, but to a point where it’s consumable, it’s appreciable by people can go a long way.

[31:36] Tim: And that’s a kind of never-ending thing, right? That maybe even becomes more and more important over time as you become, you know, have more responsibility or maybe have people under you, you work on bigger projects. You have to continually be communicating about that and articulating that. And you can’t take for granted that Even your own manager will know exactly what you’re doing.

[32:04] Brenton: Yeah, and that’s where like the trying to be candid about everything can come into play. Being able to say, I don’t know, or ask someone for help, or any of that sort of thing is really important. But I think even more important than that is establishing an environment where someone can feel like they can do that. is pretty key, um, you know, which is— we’re getting in, I guess, a tangent, like transitions into culture in terms of topic, um, but by being able to communicate and set expectations and do all stuff, you can influence culture to establish an environment that allows you to do— to be vulnerable in that sense.

[32:46] Tim: Yeah, and I think that’s a good direction to go. I mean, that’s something you care a lot about. You’re a very people-centric person, but you care about the environment that you work in and also the environment that you’re helping create with others, which I’ve seen in both places we’ve worked together. Are there other aspects, other ways in which you’ve tried to influence and shape the culture of the companies you’ve worked at?

[33:15] Brenton: So, my role prior to my newly departed role was very much sort of a very small startup mentality. There was a lot of fighting fires, working till 3 AM. It was— it wasn’t a very healthy environment emotionally, I guess. And I tried to help influence them to move to more of a prioritization communication around expectations and sort of that sort of stuff. Um, but a big part of that was very candid retrospectives, um, to the point where pretty much treating them as like a group therapy session, right? And it’s tough because you have to build that trust with the, with the people around you. And if it’s something you want to influence, I think you have to do that by example.

Um, you have to be willing to be vulnerable, uh, to the people around you to just show that you can be, that you are trying to make the environment as safe as possible for them to reciprocate. Um, be as genuine as you can when you say things like, you know, your opinion matters, or, um, how do you honestly feel about this? Um, other things you can do, I think, to help foster that is there’s the notion of like a blameless retrospective kind of thing where, you know, if something goes wrong or somebody has done something that either offended somebody or, you know, maybe wasn’t as positive or productive as it could have been, you don’t necessarily need to call that person out on that behavior or something like that.

You can just say like, you know, I’ve witnessed this sort of thing happening. you know, try and share perspective as to why that might not be really a positive thing, um, to try and give, uh, that somebody a notion of consideration and from other people’s perspectives, essentially. Um, you know, we all come to the table with a different mind, with different influences and different backgrounds and things that impact the way we view just anything. So Being open to knowing that that is the case and trying understanding everybody’s perspective, um, and what they’re coming to the table with allows them, I think, to feel heard. Um, they, you know, you may not end up in a spot that, you know, they might have wanted you to end up in, but trying to make sure that they feel like they’re in a place to express themselves.

They’re being heard and that’s not going amiss essentially.

[36:01] Tim: Yeah, that’s really helpful. What would you say is— are team game nights essential to company culture?

[36:12] Brenton: So when we were at Distil, I got pretty heavily into Fortnite. context for people who don’t know. Um, I got heavily into modern tabletop board games. Uh, I amassed a sizable collection so far, uh, but that has dwindled. So anyway, um, the way you build trust with anybody or a group of people is to spend time with them. Um, and the trick there is trying to appreciate or at least create an environment that is inviting to a bunch of different people. Um, we say game night. I, you know, we had that group. I had a group of people that came to game night. We did that once or twice a month. Uh, it became a recurring thing, and we still continued it on after we all had sort of went our separate ways. Um, but, you know, it’s not for everybody. Um, you know, same way that things like happy hour aren’t meant for everybody, you know, not everybody’s into that sort of thing. And that’s the challenge, right? If you’re trying to create a culture or work towards an inclusive culture, it’s tough because not everybody likes everything that, you know, other people might enjoy. You know, some people don’t like table hockey. Shocking.

[37:34] Tim: Can’t believe it.

[37:35] Brenton: And it’s tough, right? You want to try and make people feel inclusive. So yeah, I don’t know if I have particular advice there other than try and pay attention to the people around you. Um, you know, if they are in an environment to where they feel they can say, hey, you know, I’m not comfortable doing this, or this isn’t really of interest to me, fine. Um, the thing you want to avoid, in my opinion, is the notion of like political pressure, right?

[38:05] Tim: Oh.

[38:06] Brenton: if I don’t go to happy hour, or if I don’t go to game night, or if I don’t go to wherever, will I look like I’m not a team player kind of thing. And to me, that’s the kind of thing you want to— I would want to avoid essentially. You know, the idea is you want to create an environment that’s constructive, positive. It’s not going to be 100% inclusive because that’s very difficult to do. But it is inviting, and there’s no repercussion of not participating, essentially.

[38:39] Tim: I like that answer. I have a related question. You were my manager for this summer, and I don’t know if you have any insight into why my managers always quit. I have a long chain now of managers that quit at My current employer and a previous employer.

[39:02] Brenton: I don’t think your manager had to still quit.

[39:07] Tim: I think he just wanted to quit, actually.

[39:15] Brenton: Probably. I know it’s a joking question, but I honestly, I You know, your having been your manager wasn’t a motivator for me leaving whatsoever. But, um, you know, there are— and that’s the thing, right? It’s like there are always going to be opportunities around. Uh, part of, you know, we’re talking about career advice or any of that sort of thing. There’s always going to be jobs out there. You want to potentially— if you, if you’re interested in wanting to make a move or grow your career, and if that means moving somewhere else, um, you want to be open to opportunities, um, you know, actively or passively. Um, passively, I would say that’s sort of where your portfolio can come into play because that sort of sits there kind of like a passive income almost in terms of, uh, generating interest.

But unfortunately, a lot of times career growth happens with you moving to another position that might offer you either more incentives monetarily speaking or more career growth opportunity. And it happens. It’s not a reflection on— not necessarily a reflection on the people around you, it’s more just something comes along that is advantageous for you for your career. That is assuming that you’re not leaving because of toxicity or something like that.

[40:54] Tim: Right.

[40:54] Brenton: Which, you know, if you are, good on you because, you know, it’s tough to leave secure situations for the unknown. But yeah, you want to be open to opportunities and, you know, they’ll come around. And people move around. The benefit of that, I think, is as you move around or as people around you move, sure, you have to start new relationships and build those trusting relationships again. It’s sort of like, you know, a new— going to a new school kind of situation. But your network grows, right? If you worked with some people, they went to some other job, and somebody else went to a different job, your network grows. If all you know are the people around you at the job that you’ve had for X years, you might know a lot of people, but they’re all in the same location. So, it doesn’t really necessarily benefit you from a networking perspective as much as potentially— this is speculation, but I would assume if you had a network that is more dispersed, it’s probably more beneficial for you if you were looking to— move somewhere else or seek other opportunities.

[42:08] Tim: Yeah. I think for better or worse, that’s particularly true for those of us living in areas that aren’t the Bay Area or other big tech hubs. I live in the metropolitan tech hub of Raleigh, but you live out in rural North Carolina and you’re not just interacting routinely with people in your— not face-to-face with people in your professional orbit. And the internet’s really nice for that too, but I’ve certainly found that to be true now with 9 years in industry, knowing a lot of people at a lot of places. And not only have I moved to a variety of companies, but those people that I’ve worked with are at a variety of companies. And It just does give you really good perspective. And I’m grateful for that. And, you know, not that that’s necessarily a reason to job hop, but it’s a side benefit of moving around and being at different places.

And it’s great to have people that you can, you know, call and find out about their company or maybe even take a job with, which, I mean, that’s exactly how you came on to to my current employer last year. I was brought on and then I said, hey, we should talk to my friend who I worked with before. And it’s great when that kind of thing works out, right? That’s a nice way to get a job. Any other directions you’d like to go before we wrap up?

[43:44] Brenton: Just be open to opportunities. Be open-minded. Realize that people have different perspectives and they come to the table with different experiences, you know, and try and understand that so that way you can create a constructive, positive environment that will enable you to generate a network, you know, and potentially build a reputation that is something that can help you towards your path of success, I guess, depending on how you define success.

[44:15] Tim: Very nice. I like it. Do you want to tell people where they can find you on Twitter or your blog?

[44:22] Brenton: My blog is brentonmallen.com. My Twitter, I think, is Brenton Mallen.

[44:28] Tim: You should know this by now.

[44:29] Brenton: I think I’m on Instagram more than I am on Twitter these days. I like photography, as we know. So yeah, I’m somewhere around. LinkedIn’s pretty good.

[44:38] Tim: You are a man of many interests, and your Instagram is a good follow because you can find out about woodworking, stained glass, board games, dogs, flowers, all kinds of things.

[44:49] Brenton: Yeah. Yeah. Uh, well, thank you for having me. It’s been fun.

[44:57] Tim: Thanks for coming on Into the Hopper.