Denise Gosnell 0:00 And so I had to sign up and take a class called graph theory. And I honestly at the time thought it was a course on studying bar graphs and pie charts. I kid you not. And so I was I was walking into class that first day thinking that's what I was going to do for a whole semester. And that's when I met Dr. Theresa Haynes. And within the first five minutes of class, she had drawn nodes and edges and she was talking about relationships within data. And my mind just opened up to this new epiphany or this new way to think about and correlate relationships and data. That still kind of gives me goosebumps today when I get to recall that story because it's it's so fascinating to me.
Tim Bourguignon 0:54 Hello, and welcome to DevJourney, the podcast shining a light on developers lives from all over the world. My name is Tim Bourguignon, and today, I received Denise Gosnell. Denise is the chief data officer at data stacks, where she helps build some of the largest distributed graph applications in the world. Her passion centers on the application of graph data, and complex graph problems. Denise holds a PhD in computer science from the University of Tennessee, where her research coined the concept of social fingerprinting. Since then, she has built publish on pretended and spoken about dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals. Denise, welcome to DevJourney.
Denise Gosnell 1:50 Yeah, thank you so much, Tim, for having me here. I'm really looking forward to this conversation. And I'm thrilled to have
Tim Bourguignon 1:55 you on so let's go all the way to your childhood. Maybe when no How did stem and computer sciences enter your life in the first place?
Denise Gosnell 2:04 I think that's a fascinating first question. And it's got a very unique answer maybe I remember as a child that I had this affinity for bringing together math and art. And I would play with construction paper. And I would, I would take red construction paper and blue construction paper and I would make like a tic tac toe board on that on the pieces of construction paper, and I'd fill in numbers in every little square within the Tic Tac Toe board, one on red, and then one on blue. And then I would add up the numbers into another Tic Tac Toe board on some purple construction paper. And this is just something that I did as a kid around my house and my parents thought that I was very strange, but I just had this affinity for working with numbers and I've always loved Having the ability to understand data and to use data to communicate something to somebody else. So that for me that that's kind of one of the most earliest memories that I can recall of working with the general STEM field in my life.
Tim Bourguignon 3:14 Does it run in your family?
Denise Gosnell 3:16 It does, it does. My, my mom was she worked with data as well also a mathematician and she led a really awesome initiative for a big company here in the United States. And so I I find that I get a lot of my thought processes and approaches to working with with numbers from from her.
Tim Bourguignon 3:36 So it was no brainer you went in Stan your studies?
Denise Gosnell 3:41 You know, I think that's I think that's, that is true, it was a no brainer, but the, the direction that I wanted to take with it was really hard for me to find
Tim Bourguignon 3:51 also,
Denise Gosnell 3:52 so I don't know, I guess, when, when you're younger, and you really like math, especially when you're growing up, you know, you know, a few decades ago, to me, I only thought that I there was really one option for the type of career that I had. And I honestly thought that the main path I was going to be getting into if I loved math this much was going to be teaching. And so when I, when I went to my undergraduate institution, the the College of Wooster in Ohio, in the United States, I immediately identified that I was going to study math, but all of the mentors who had been helping me pretty much showed me that the main career option for me at the time was going to be to teach. And so at the time, I was very lucky that my institution required me to major in the subject matter and get a teaching certificate on the side. And I say lucky because teaching ended up not being not being something that I wanted to do for my whole career, especially being 20 years old, at the time trying to teach high school mathematics. It was quite the failure if you asked me.
Tim Bourguignon 5:03 Like, you've caught up with that you you've been on stage quite a few times recently, haven't you?
Denise Gosnell 5:08 Yes, I have. And I think that I think being on stage recently or teaching professionals is much different than it's a much different perspective than thinking that teaching is completely confined to a classroom environment. And the way that teaching is kind of orchestrated here in the United States. To me, they're two completely different experiences. And one of which I gravitate towards and love having the opportunity to do that work here at data stacks. And I'm just I'm really grateful for having made some of those pivots on the way to find my feet, though it did take a long time.
Tim Bourguignon 5:45 Oh, we'll get there. Let's go through your studies first. So, um, you were able to study math? Yes. And to study teaching on the side and not not mixed both of them right away. Is that correct?
Denise Gosnell 5:58 Yes. That's That's correct.
Tim Bourguignon 6:01 And so you went all the way to your master's thesis and then started working on graph theory, or did you study graph theory before?
Denise Gosnell 6:09 I actually Yeah, that's it. That's a great question. So when I was studying undergrad, that was when I kind of came, came to find computer science for the first time. I took a computer science course and honestly did terribly in it. But towards the end of my undergraduate career, I wrote my first program that did that did encryption with elliptic curve cryptography. It was a very fascinating and fun project that I that I enjoyed doing as a part of my undergraduate studies. But the the teaching part kind of comes back more as one of those moments of adversity that kind of where it was a fork in your career, because once I completed this really fun, deep technical work, studying elliptic curve cryptography and applying it to different encryption techniques, I kinda was dumped into the world, like I mentioned earlier as being a teacher. I quickly found that that was a fork I didn't want to go and I didn't want to take that fork. So I, I through, you know, through connections was able to be awarded an NSF fellowship in the US to, to go work under Dr. Theresa Haynes, a very well renowned graph theorist. And Dr. Theresa Haynes was at East Tennessee State University and her specialty is in graph theory. So by journey of having to put teaching down and make a choice to go get a master's in mathematics is how I found my way into the the graph theory space.
Tim Bourguignon 7:34 Interesting. So it wasn't really a choice or a passion of yours. It's just came to be and and develop itself.
Denise Gosnell 7:42 Yeah. And yeah, and I guess after the fact, the best way I can describe it is that I had made a decision to explore a career option or a fork, you know, a choice that you could do for your career. I realized that wasn't the right choice for me. So I had to try something new and When I tried that new thing it was it was graph theory. And I think, Tim, one of my favorite stories, actually, when I was getting ready to start my master's degree in mathematics, I was, honestly, I was locked out of registering for the courses that I wanted to take that I had to take for, you know, the required courses for your degree. And so I had to sign up and take a class called graph theory. And I honestly at the time, thought it was a course on studying bar graphs and pie charts. I kid you not. And so I was I was walking in the class that first day thinking that's what I was going to do for a whole semester. And that's when I met Dr. Theresa Haynes. And within the first five minutes of class, she had drawn nodes and edges and she was talking about relationships within data. And my mind just opened up to this new epiphany or this new way to think about investing correlate relationships and data. That still kind of gives me goosebumps today when I get to recall that story because it's, it's so fascinating to me. This is indeed a
Tim Bourguignon 9:09 great story. I completely understand the mix of it's just a combination of words. If you have never heard of it, then you have to make something out of it. Yeah.
Denise Gosnell 9:21 Yeah. Yeah. And I guess I outed myself that I'm not that type of student who reads the textbook before your first day.
Tim Bourguignon 9:29 I didn't either. So it's just between the two of us. I'm not sure how the how the the academic world in the US is the picture I have from from from France and Germany, where I've lived so far is a lot of students that go into PhDs and go into the the long academic route and stay in there and kind of remain in this in the theoretical analytical and researchy world? Um, is it? Is it the same first question would be, is it the same in the US? And the second would be when when did you decide to fork and go toward the industry?
Denise Gosnell 10:13 That Tim, that's such an observant question. And being a data driven person, I would want to go back and, you know, kind of survey all my colleagues and see and take a mystical approach to this. But just just kind of running through my mind quickly, that, you know, anecdotally, that is very true. The majority of my colleagues that, you know, we went through our PhDs together, very few of us chose to leave and end up working in industry. So yeah, I do find that to be anecdotally true. But But what I guess is not typical is that for me, I never had the aspiration to stay within academia, you know, for a long amount of time. I was always very drawn to the application and the connection from a concept to how it helps a human. And sure, I know there's topics where you can study that for your career with an academia, but I, I just really find passion and drive and applying it. And and so to me, that was never really a question. I mean, maybe one day I'll go back and be a professor, especially if there's an opportunity maybe to teach something that I just really want to teach, which maybe that'll happen. But for my early career, I've always wanted to leave. And I think I think that was that came from two core motivations. The first of which we already talked about how I had tried teaching and I found off really found out really early that teaching just wasn't for me. But then there's the second side and to me, it's, it's about what I've valued as a student. And my favorite teachers along the way, be it all the way back into high school or early college or even when I was studying at The University of Tennessee for my PhD. My favorite professors were always those, which had really deep application experience where they made me feel like they were teaching me something relevant to the real world. And anytime I had the chance to learn from that Professor, or I felt like they had been there, they'd had their hands on it out there. And whatever it meant to be the quote unquote, real world. I just, I really wanted to follow those footsteps so that if I do end up teaching later, I could bring that experience back to my students as well.
Tim Bourguignon 12:33 Makes perfect sense. Absolutely perfect sense. It really adds up with was where you you ended up now doing doing data science, which is exactly the best application of pure mathematics that I can have. I think
Denise Gosnell 12:50 I would, I would agree with you, you and I would be well aligned on that topic.
Tim Bourguignon 12:56 So do you mind taking us to this move toward the end How did that go? How did you decide where you wanted to go next? Um, how did this this first confrontation with the industry, work out, etc?
Denise Gosnell 13:10 Yeah, that's that. That's another really great question. And I feel like there's a theme that you might pick up on. And now I'm just going to call it out. I feel like a lot of the decisions that took me down a certain path, they were forks, and they were options that I decided to try and then found out it wasn't for me, so I went back and just tried something different. And that that exactly describes how I got started with my first my first full time job outside of academia. Towards the end of my PhD, I did have some really awesome internships where we I was working alongside doing research, etc. But I didn't really feel like an actual real job because to me, it's more about what does that step look like when you leave the blanket of academia and do something full time outside of school. And, to be honest, I had a I had, I was extremely lucky and privileged at the end of my PhD to have a ton of fantastic offers from some of the top companies in the United States, you know, IBM, Microsoft apple. And at the time, I was working on moving to Cupertino with my husband. And I was learning more about what my potential role could be as a software engineer. And I I honestly just realized that that wasn't the type of work that was going to make me happy. And I had a phone call with a gentleman whose name is his Ted Tanner. And he at the time, my PhD advisor was on a board of one of his companies. And within two minutes of talking to Ted Tanner, he had opened up my world to this entire ecosystem of open source software and bigger ideas and bigger projects that you get more from the entrepreneurial mindset. And so instead of Moving to Cupertino, I decided to take a leap of faith and joined a startup, the startup at the time was called pocket doc. And I was super, super lucky to be asked to lead lead a team that was building a graph database that mapped out the entire economics of health care in a graph. And at the at the time, that's that was the business of pocket doc they were processing you know, all the all the transactions that you could have within the healthcare industry. And I was you know, using some really awesome and fun open source tools to to model that out, look at the economics and the changes and changing behaviors that you can get from that data. And it was a project that I To this day, still have these really awesome awesome photos of of the graphs that we created, etc. happy to share those with you after the after this podcast.
Tim Bourguignon 15:56 I would like to come back to one thing you said he had a ton of offers from from companies, and then you say, as a software engineer, Mm hmm. And do you make a difference between the definition you had of a software engineer back then and what you were searching for and what you're doing today? do you differentiate between software engineering and data science or just just trying to understand what was behind this this term software engineer that's used?
Denise Gosnell 16:21 Yeah, I think I think that's a really good distinction. And my perspective, again, is just one person's journey. So I'm sure someone else in your audience might have different experience. But to me, I've learned that software engineering, really great problems, but their problems more oriented around the logic of the tool that you are building. And whereas what I was looking for was was wanting to work more with data and using data to answer questions or architecting. pipelines that were more focused on data quality, cleanliness, accuracy, etc. so that you could derive really cool insights from that data. And to me there was there was two very clear directions I could go, one of which was to be a software engineer at Apple, or the other of which was to, you know, lead a lead and build a Data Science Initiative at a startup. And the idea to work more on the application side of how to use data to answer interesting questions, was something that was more core to what I wanted to do. So that to me, that was an easy choice, because I thought that the I thought that in and I was correct. I thought that working with data would would be more enjoyable and make me want to wake up and get to work every day.
Tim Bourguignon 17:35 Make sense? Make sense? This is really the definition of data scientists that I had in mind. But yeah, that was 2015. Right?
Denise Gosnell 17:42 Yeah, yeah. Yeah. At the time is about 2014 2015. Yes.
Tim Bourguignon 17:46 The the big boom of of data science was was just emerging.
Denise Gosnell 17:50 Yes. Yeah. So
Tim Bourguignon 17:52 so let's go let's go to two pocket back. How was it you to get in the industry to to not work on on a pgcs anymore to not do research but you have probably customers or maybe lack of customers breathing down your neck doing data science, but in this different context, how did that feel?
Denise Gosnell 18:12 It was the most challenging job I've had to date. Working at pocket doc and having the privilege to get to work with the leadership there. It challenged me in every facet of that of that job. And to be honest, it it made it got it, it just it made me so much better of an engineer and a thinker today because of that opportunity. And I think the very first biggest hurdle for me was, was moving very fast. It I thought that I moved quickly in academia, but there was there was a difference. When I was when I was in graduate school. I did move very fast and I made a ton of decisions, but I was making fast decisions for a very wide group. of projects. So every individual project was moving slowly, but I was keeping all those projects moving. So my work felt quick. But when you moved into working in a startup, you have laser focus on your, you know, single product that you're building and, and you need to make that move extremely quickly. And specifically, what needed to move quickly was was my personal development understanding of software engineering processes, the tools that we needed to get up to speed on etc, I was so used to just coding everything from scratch and see and, and now we needed to work, work with already custom developed API's and you need to understand somebody else's perspective on the tool that they published, and how to, you know, make that tool fit the problem that you're solving. And so that that was a whole new set of muscles that I had to exercise, and I'm really glad that I did
Tim Bourguignon 19:57 moving very fast. And that's a sign are two things that in my mind collide a little bit? When, when a picture does the science I picture massive amounts of data, which are not necessarily completely cleaned up and usable as is you have to Maliks the data to get at the point where you can stop doing real science on it. Yeah. And I have the idea of biases. And is the data really answering or really able to answer the questions that you want? Or are there biases in there? And if you want to move fast, and in parenthesis and break things, and it's basically gonna break so how do you reconcile dues to dues to faces?
Denise Gosnell 20:42 Yeah, I think that's fascinating. And Tim, maybe you need to go a little deeper and be understanding your perspective. So let me see if I can. I also, here's how I see what you're saying. I see there's the idea of building fast versus the idea of getting accurate answers fast. is, Are those the two different items that you're discussing?
Tim Bourguignon 21:04 Kind of?
Denise Gosnell 21:05 Okay. Yeah. For me in the world of data science when it comes to moving fast, so much of the job that you have, and by so much, I mean, you hear all the time, it's 80%. Like it really is, it's more probably 80 90% of your job is data cleansing. And you need to be able to massage and move quickly through all those processes so that you can just get your data ready to answer questions. And that's, that's the area that I think was a little bit more surprising for me, coming from academia into industry was just how much went into cleaning data. Now, I say that with the caveat that the data I worked with for my PhD was tell it was telecom data. To this day, I love telecom data, because it's so clean. If you don't have to deal with all of this at least at least the call detail records that I was working with They were very clean. I'm sure there's plenty of areas in telecom where you don't have clean data. But the data I worked with was so clean that I felt I almost felt unprepared for the challenges of working with healthcare data, which has got to be some of the messiest data I've ever seen. Still in the United States, so much of the healthcare data is is not digitally recorded, that we were trying to work with, or it had been really haphazardly transformed into a digital representation. So getting any type of structured information out of it was just a bear. It was very difficult. So when I talk about moving fast, it's it's you've got to have that mentality that that can quickly iterate through cleaning your data. Otherwise, you're just going to be stuck there for months before you can actually get insights. Where your team lead or your or your boss or whoever's in charge of the project is breathing down your neck for insightful answers. To the other point, getting answers quickly. So it's, it's you've got to you've got to really balance that. You've got to You have to balance your understanding of how difficult it is to get an answer from end to end. And by that I mean taking it from unstructured messy data into an insightful analytic and understanding how difficult it is for just one piece within the entire data science process, which needs thousands of those pieces to be compiled together to bring an insight. So moving fast for me was just more about juggling so many of those different those different problems to really drive towards getting out useful information. Does that help Tim?
Tim Bourguignon 23:37 Yes, that's, that's okay. What I was getting at.
Denise Gosnell 23:39 Okay, cool.
Tim Bourguignon 23:41 Um, did you get help during this time?
Denise Gosnell 23:43 Oh, of course. What kind of help? Do you mean, though?
Tim Bourguignon 23:45 Do you have mentor? Did you have people that did this before that could could show you or lead the way a little bit, or did you have Do you have people in your team that that had done this before? Yes, I was just running into them before. of persons,
Denise Gosnell 24:01 there's, there's going to be a little bit of both, there's going to be a little bit of working with someone who's been there before and can help you. And then there was almost equal if not more amount of trying to figure things out for the first time. And we had a fantastic team data science team I was working with at the time, with with, with some, with some gentlemen who had really fantastic experience with using using tools and helping break down complex problems. But on the other hand, I was very excited about working with the Titan graph database, and this was back in 2014. And there was not a lot of information out there, or a large community of people who I could talk to about understanding distributed graph databases. And then even you know less resources on how to use the Gremlin query language to get used information out of the graph database. And that part was, I mean, for the lack of a better word, it was lonely. I did have another one or two gentlemen who are working alongside and we were trying to figure it out together. But really, there wasn't anyone in front of us who could tell us what it looked like. Because we were really kind of plowing out new Greenfield,
Tim Bourguignon 25:26 did you feel like you're like a PhD over again?
Denise Gosnell 25:31 That's a good question, actually. And Tim, that's not something I had thought of before. If it felt like that all over again, to be honest, I think it felt like a different. I don't know if I would say that. And I think because I think it didn't feel like that. Because with my PhD work. I had so much control of getting from a you know, of understanding what the data was and running some really cool machine learning algorithms. Graph algorithms for doing some insightful calculations, that once I set up one process, I didn't need to repeat it. I just needed to work on the analytics. Whereas the type of software that we were building at the time when we were working with the Titan graph database, that work felt that worked out never ending, like the data architecture of what we were trying to build, kept growing. And we kept needing more information. It felt like a actually a bigger project that are focused PhD, to be honest.
Tim Bourguignon 26:31 What happened to the company is still still up and about and running. Oh,
Denise Gosnell 26:36 yeah. Well, the the company was acquired, which is always great to get to say that your startup had an exit. Pocket doc was acquired by change healthcare here in the United States as a part of some of the blockchain technology that we began to build out with with creating identity by consensus, through some partnerships that we had built up with Intel. And you know, the I think this was the 2016 2017 when this acquisition happened and in change healthcare at the time, I was looking forward to using our assets to see how blockchain can help out the healthcare industry. Though when that acquisition happened, I had already left pocket doc and come to data stacks. So I wasn't there when the Aqua acquisition occurred
Tim Bourguignon 27:21 just before we come to today's dive on your profile on LinkedIn, even last position is Pocock is Senior Technical evangelist. Yes, yes. This sounds odd.
Denise Gosnell 27:32 Why do you think it sounds odd?
Tim Bourguignon 27:34 When is he when I think about an evangelist is think about somebody who is not necessarily creating the bleeding edge more telling what others are doing in a way that is that is understandable that is accessible for the for the broader masses cetera? And what I've heard up to now from from your story, is you were searching for the bleeding edge. He was searching for You also haven't been tried before. And this sounds odd to me. Did you understand?
Denise Gosnell 28:07 Yeah, I do. I do. I do. Tim, thanks for sharing that that perspective. That's That's very interesting. And I mean, I'm sure you're aware of and many people in your audience like when when you're working in a startup, to be honest titles, titles don't really mean anything. So at the time, the the work that I had the opportunity to be involved in at pocket Doc, we did start with building a distributed graph database to map out the economics of health care. And we started also developing through some really awesome partnerships that we were building, we started developing some identity by consensus designs, like I mentioned earlier for for trying to implement different health care transactions on a blockchain and this is 2016 2017 when you know you couldn't walk around, so Francisco and not here's the word blockchain over breakfast. So we were working on that technology and it was a really fun group. And as a part of that is essentially kind of morphed into a little bit of a role where I was becoming a public public advocate for for the work that we were doing, and was, you know, speaking on behalf of the company and and helping drive the, the consortium that we were putting together with some, some really big companies. And so it was just it was a new role to exercise and I got a lot of fantastic experience with public speaking and, you know, creating advocacy. But for me, I again, it was a journey that I decided to give, give a try, and I tried it out. I'm very passionate about talking about tech. So I've definitely made that a small corner of the role that I take with me and brought here today to stocks as well. But I did want to kind of just take a sample of it, but get back to doing You know, more more technical related content where you are building or you know, kind of leading some of the green field like you mentioned. So your observations completely completely accurate but I feel like that is also another one of those forks I had in my career when I decided to try something and learn from it and then try something else.
Tim Bourguignon 30:21 Cool. Cool. So let's come to datastax Yeah. How did you get there?
Denise Gosnell 30:26 To be honest, I called I called Mateus. I at that I mentioned that I had been working with Titan at pocket doc and so I had met, I had met Mateus broche Allah and Marco Rodriguez and and the the original a religious team. I had met them through working with their technology or at conferences. And so I I just I just emailed Mathias and I said, Hey, you know, you guys recently got acquired by data sacks. Can I come work for you guys? And so that's how I got here. It really was as simple as that.
Tim Bourguignon 31:00 Okay, and what attracted you in the company from from the outside? I mean,
Denise Gosnell 31:04 I mean, there's from for me, it was so evident to see that data stacks of the company was acquiring and holding the leading experts within the graph space. And it's something that I was very passionate about, as soon as I learned that it wasn't bar charts. And I wanted to I wanted to be a part of it. I wanted to be here and I I wanted to see where the leading company, in my opinion was going with graph technology. And so I decided to follow him follow that team here. Now might be the right time to define quickly with with data stacks really does. Yeah, that's a good point. So datastax is the leading contributor behind the open source database, Apache Cassandra, and overall we're a company that is that is leading the NO SEQUEL movement here in the United States from innovating with data. And database solutions
Tim Bourguignon 32:01 and what is your your role in in there?
Denise Gosnell 32:03 Yeah. So when I when I joined data stacks, you know, back in Gosh, it's been two and a half years. And now that we're in 2020, that math is not something I've done at all. So it's a 2017. I came to data sacks and I started I started their graph practice here. And as just a part of the overall overall evolution of data stacks his journey and being leaders within the NO SEQUEL industry started working on helping our product and our people here at data stacks, understand graph technology, understand its application throughout any industry and how it can help different companies and different users solve really cool, really cool business problems.
Tim Bourguignon 32:49 It's sued the first time since the beginning of the book, because as you mentioned, this open source again, you mentioned at the very beginning, but not in the in the middle was an admission or was it did you do Have a contract with open source all the way or is it something that just came back into your into your life through the back door?
Denise Gosnell 33:05 I think it just in this story just kind of came back into my life I've ever since I left academia and moved on from, you know, focusing on just purely analytical work I've been contributing two or more using not necessarily I'm not a contributor or committer. But I've just I've been a user and an advocate for open source software. And that's that journey definitely started at papa doc when I was working with the Titan graph database. And another major another major reason why I wanted to come here and work at data stacks. I mean, I did tell that story that I was very interested in coming to work with some of the best minds in the graph space. But I also saw how much work that data stacks was putting into Apache Cassandra and they are they were in are continuing to lead such an integral product for in database for you know, making it Easier to scale to scale your application that I wanted to also be part of that, that new initiative that is really transforming, transforming the NoSQL industry. So it was both the combination of their graph leadership and then also their leadership data stacks is leadership with, with building and contributing to open source software like Apache Cassandra, that made me really want to be here. How
Tim Bourguignon 34:23 does it feel to to work with with brilliant people?
Denise Gosnell 34:28 That's Tim, that's a that's a great question. I can say, Oh, that's such a hard question. Definitely at the beginning, when, at the beginning of my journey, as an engineer, data scientist, it was very intimidating, to, to work to work with such brilliant people. But ironically, the intimidation and that that imposter syndrome that you hear people talk about was not a bad Part of my career here at data stacks. I would say within the first few months of being here, though I was very before I got here intimidated by the brilliant minds that I knew worked here. Once I got here and and learned, learned about the culture here at datastax. And I just kind of got integrated into how we process and do work together. It really helped kind of melt away that imposter syndrome or melt away that intimidation. And it was just a fascinating, fascinating part of my journey that I honestly was very unexpected. Because you know, you go to get to work with the best people every day in the industry. And now that I've been here for two and a half years, it doesn't doesn't feel like that at all doesn't feel intimidating. Everyone's extremely welcoming. And I don't know you hear people say that all the time that you're co you know that your colleagues and peers at work are welcoming but that truly is a part of our culture here. We're very humble contributors to how we work and we like to collaborate and make sure that opinions are heard as we move forward when we're problem solving. And it just really helped take that intimidation factor that was perceived on the outside and is absolutely not a part of what it's like on my day to day. Sorry for the long winded answer, but it was I hadn't thought about that before.
Tim Bourguignon 36:29 No, absolutely, absolutely. I hear this so often. You want to feel the the the dumbass in the room. So you work with people that are challenging you all the time. But there is this other side of the of the of the metal, which is it's intimidating. It's it's imposter syndrome all over again. And I find if you're awfully hard to jump both feet first in such in such a context, it would be I would be really scared.
Denise Gosnell 36:56 Yeah, yeah, I agree. And I I don't know where I, how I developed this in my life, but the many forks we've talked about in my career journey. They they stay stemmed from a willingness to fail, that I have, and I don't mind failing, because I find that I learned so much when I fail. So to me, I feel like intimidation or being worried about working with people comes from a perspective of fear of, oh my gosh, what happens if I don't, if I fail? Or what happens if I work with this person or I go try and work at this company, and it doesn't go the way it's planned. And I've always been very open to letting things evolve and been open to coding until you break things. I guess it's one of the one of the ways to think of it so I think that that mentality helped me you know, quickly overcome. You know, any type of perspective I would have had and you know, there there's a lot that I can say about it. How fascinating and brilliant it's been to work with people here. And you know, especially the past two years, getting to work with Mateus. On the book we've been writing, how that's really just been helped just helped evolve my mindset on what it really means to perceive, perceive your work.
Tim Bourguignon 38:16 Thank you. Thank you for you for the advice I would like to ask you for for another advice. And if, let's say we had listeners who are at the end of their academic studies and are considering going into the industry, what would be the one advice you would give them
Denise Gosnell 38:32 if they're at their end of their academic career and going into industry? that's a that's a great question. And I think that one of the common themes I've had is that I've that I've always trusted myself. So the the piece of advice I would give to to your audience is to trust themselves to and if they don't know what that feels like now then to to figure it out to figure out what it sounds like to trust yourself. Because for me, that's been my truenorth I've been pretty in tuned with the types of opportunities I want to go after and the experiences that I think would be valuable. And I trusted that I could find my way to get there. Amen.
Unknown Speaker 39:12 Thank you. Yeah, of course.
Tim Bourguignon 39:15 If dues, dues listeners that are at the end of the academic career in science or others, I wanted to, to start the discussion or continue the discussion with you, where would be the appropriate place to do that?
Denise Gosnell 39:27 Yeah. And I, I would love to have a conversation. I'm I'm always open for learning about what people are working on. And the the best way to get a hold of me is on Twitter. I'm very active. And my Twitter handle is at Denise k Gosnell on Twitter, so you can find me there. And if you want to dig into any of the code that I'm most recently working on, I'm always pushing that up on GitHub, where I think most recently, you will find a massive profile or a massive project on GitHub around the world. With distributed graph databases, and it's all of the code examples and queries that go alongside a book that I'm getting ready to publish in the spring of 2020. So you can find that both from my Twitter profile or by finding me on GitHub with the same name at Denise Kay Goss now and beside the the upcoming book, what's, what's in your near future? What do you have on your plate? Uh, more than more than I want to admit? So I'll be I'll be speaking at many conferences this upcoming spring 2020 and into the summer. So I know that I will be at many of the O'Reilly events, because the book I'm publishing is the practitioners guide to graph data. It's being published with O'Reilly and my co author is Dr. Mathias brusha. The gentleman that we've spoken heard a little bit about throughout this podcast, and the two of us are we're launching our book and then we'll be speaking at some events around the world. And looking forward to looking forward to getting that word out there. I'm sure you are. Yeah.
Tim Bourguignon 41:01 Awesome Anything to add?
Denise Gosnell 41:03 And oh gosh, Tim No, I've thoroughly enjoyed this conversation happy to to come back at any time if we want to, you know, dig deeper into any other topics because this has been a very fun, fun podcast to be a part of. So thank you for having me.
Tim Bourguignon 41:14 And this has been another episode of developer's journey. We will see each other next week, bye. All right, this is Tim from a different time and space with a few comments to make. First, get the most of these developer's journey by subscribing to the podcast with the app of your choice, and get the new episodes automagically, right when the air. The podcast is available on all major platforms. Then, visit our website to find the show notes with all the links mentioned by our guests, the advices they gave us, their book, references and so on. And while you're there, use the comments to continue the discussion with our guests or with me, or reach out on Twitter or LinkedIn. Then a big big THANK YOU to the generous Patreon donors that help me pay the hosting bills. If you have a few coins to spare, please consider a small monthly donation. Every pledge, however small counts. Finally, please do someone a favor, tell them about the show today and help them on their journey.