Kristie Rowley | Careers in Data Science

1 00:00:00,938 --> 00:00:03,396 [Anna]: You're listening to the Real Intelligence podcast 2 00:00:03,855 --> 00:00:08,884 [Anna]: presented by RXA, a leader in business intelligence and data science consulting services. 3 00:00:09,422 --> 00:00:12,156 [Anna]: We're here to bring attention to the unique stories, 4 00:00:12,574 --> 00:00:13,074 [Anna]: perspectives, 5 00:00:13,532 --> 00:00:19,996 [Anna]: challenges and success that individuals in the data industry face at every career stage. Welcome to the show. 6 00:00:21,054 --> 00:00:23,033 [Anna]: Welcome to the Real Intelligence podcast. 7 00:00:23,691 --> 00:00:36,370 [Anna]: You're on with Jason Harper, CEO and Founder of RXA and Anna Schultz, Marketing Coordinator at RXA. Our guest today is Kristie Rowley, the Principal Data Scientist and Director of Data Science at Domo. 8 00:00:37,350 --> 00:00:45,860 [Anna]: Kristie earned her PhD from Vanderbuilt University and has worked as a data scientist an organizational behavior consultant for over twenty five years. 9 00:00:46,438 --> 00:00:53,190 [Anna]: She has used data science and machine learning to effectively inform the business decisions and strategy of numerous organizations, 10 00:00:53,770 --> 00:00:55,670 [Anna]: including Fortune 500 companies, 11 00:00:56,330 --> 00:00:57,510 [Anna]: nonprofit organizations, 12 00:00:57,864 --> 00:00:59,879 [Anna]: governments and policy institutes. 13 00:01:00,575 --> 00:01:06,915 [Anna]: Dr. Rowley also specializes in data science, machine learning, and artificial intelligence education. 14 00:01:07,770 --> 00:01:15,010 [Anna]: Throughout her career, she has designed and taught graduate level statistics, data science, and machine learning courses at numerous universities. 15 00:01:15,783 --> 00:01:20,711 [Anna]: She has translated this experience into our current role at Domo by working with organizations 16 00:01:21,527 --> 00:01:22,266 [Anna]: to effectively 17 00:01:22,820 --> 00:01:26,280 [Anna]: develop and scale up their production data science processes and practices. 18 00:01:27,060 --> 00:01:30,880 [Anna]: Her work can be viewed in more than twenty five peer reviewed research articles 19 00:01:31,234 --> 00:01:36,583 [Anna]: that demonstrate her passion for data science and its many real world applications. Welcome to the show, Kristie. 20 00:01:37,621 --> 00:01:41,568 [Kristie]: Thank you. So nice to be here. Thanks for having me. 21 00:01:42,465 --> 00:01:48,330 [Anna]: Yeah, absolutely. So that's an incredibly impressive bio, and I know it only kinda scratches the surface of your accomplishments in your career. 22 00:01:48,944 --> 00:01:57,085 [Anna]: So we like to kinda start off our podcast by getting to know the real you. So I have a couple questions for you that we might not find the answers to you in your professional bio. 23 00:01:58,100 --> 00:02:01,060 [Anna]: The first one being, what's the one thing you'd love to be an expert at? 24 00:02:03,420 --> 00:02:04,780 [Kristie]: Oh, do I have to pick one? 25 00:02:05,672 --> 00:02:09,139 [Kristie]: You can give us a few. 26 00:02:13,380 --> 00:02:15,080 [Kristie]: Everything that I want to try, I want to be an expert at! If I had to pick one, 27 00:02:18,740 --> 00:02:21,840 [Kristie]: oh, I don't know. I think I would be, like, the 28 00:02:22,273 --> 00:02:25,004 [Kristie]: world's best guitar player. I think that's what it would be. 29 00:02:26,736 --> 00:02:27,435 [Anna]: That's awesome. 30 00:02:28,409 --> 00:02:33,400 [Anna]: Do you have any experience with other music and musical instruments or just kind of guitar? 31 00:02:33,980 --> 00:02:37,720 [Kristie]: Oh I grew up playing the piano. So I still play the piano today. 32 00:02:39,035 --> 00:02:44,995 [Kristie]: And I was classically trained in piano. But pianos are really hard to take to college and things like that. 33 00:02:46,049 --> 00:02:48,787 [Kristie]: So I started playing the guitar when I was pretty young. 34 00:02:49,246 --> 00:02:52,703 [Kristie]: I played a number of other instruments, but those are my two main instruments. 35 00:02:53,162 --> 00:02:56,753 [Kristie]: And then in terms of music, I like to song write and 36 00:02:57,732 --> 00:03:01,747 [Kristie]: and sing. So those are my four things in music, I guess. 37 00:03:02,166 --> 00:03:02,906 [Anna]: That's awesome. 38 00:03:04,217 --> 00:03:17,476 [Anna]: My second question is you mentioned to us in our previous discussion that you always wanted to be a lawyer and actually turned down a couple opportunities to attend law school. So what kind of changed your mind and drew you to a career in data? 39 00:03:18,474 --> 00:03:22,246 [Kristie]: That is true. So I am from a very very small town in Idaho. 40 00:03:22,919 --> 00:03:34,945 [Kristie]: And if you are an intelligent young women, woman, the two things that you can grow up to be is a lawyer and a doctor. Those are the two occupations that you know are professional jobs when you grow up. So I picked a lawyer. 41 00:03:36,005 --> 00:03:40,400 [Kristie]: Lots of jokes when I was growing up that I would make an excellent lawyer because I argued about things a lot, 42 00:03:41,058 --> 00:03:44,470 [Kristie]: particularly with my father who argued right back at me. So... 43 00:03:46,044 --> 00:03:56,216 [Kristie]: So I thought that's where I was headed, to law school. And I had two opportunities to go. That's what I was going to do and just kind of decided it wasn't for me. I 44 00:03:56,529 --> 00:04:10,450 [Kristie]: did some internships with some law firms and I got to shadow a few lawyers and found out that while I was really fascinated about what they did, and I would love law school, I don't know that I would have enjoyed being a lawyer. 45 00:04:10,829 --> 00:04:14,965 [Kristie]: So I changed my mind and I went the academic route and 46 00:04:15,758 --> 00:04:17,314 [Kristie]: started doing research a lot more. 47 00:04:18,551 --> 00:04:20,647 [Anna]: Cool. That's a really cool story 48 00:04:21,025 --> 00:04:26,169 [Anna]: to kinda hear what drew to that path rather than, you know, it sounds like you had a lot of options 49 00:04:26,507 --> 00:04:28,244 [Anna]: that you could have gone down. So 50 00:04:29,261 --> 00:04:40,370 [Anna]: you know, we do know that you did end up going down this route towards data and have made a really awesome name for yourself in that. So can you start off by telling us a little bit more about yourself and your career journey? 51 00:04:41,605 --> 00:04:42,105 [Kristie]: Oh, 52 00:04:42,885 --> 00:04:44,185 [Kristie]: the word I would 53 00:04:44,605 --> 00:04:46,785 [Kristie]: say to describe my career journey is meandering. 54 00:04:48,045 --> 00:04:48,545 [Kristie]: I just 55 00:04:49,019 --> 00:04:50,157 [Kristie]: meandered all over 56 00:04:50,974 --> 00:04:53,669 [Kristie]: and tried to figure out where I would fit best. 57 00:04:54,446 --> 00:04:56,942 [Kristie]: I think partially that is 58 00:04:57,614 --> 00:04:59,952 [Kristie]: a function of being a woman. 59 00:05:00,450 --> 00:05:02,148 [Kristie]: Partially, I think that 60 00:05:03,086 --> 00:05:11,790 [Kristie]: we often tend to meander a little bit before we get to where we're going, we often grow up thinking that we'll be in careers that might be gendered 61 00:05:12,325 --> 00:05:15,785 [Kristie]: in some kind of way. And figure out that we have a lot of talents and 62 00:05:16,165 --> 00:05:19,505 [Kristie]: haven't exactly received the training or the 63 00:05:20,459 --> 00:05:29,210 [Kristie]: mentoring along the way to propel us into a different career. And so I came to a place where I realized I can be good at a lot of things. 64 00:05:29,945 --> 00:05:49,977 [Kristie]: And it's really up to me to decide which one that I want to do, and which one that I'm really passionate about. And when I figured that out, it was really freeing. I wasn't, I didn't pigeon hole myself into anything. I had talents. I had abilities. I could use them in a whole bunch of ways, and I was fortunate enough to have a lot of mentors along the way 65 00:05:50,355 --> 00:05:58,769 [Kristie]: that whether they know it or not, and sometimes it was really insignificant things, they allowed me to believe that I can do things that I don't, I wouldn't have come to on my own. 66 00:06:00,149 --> 00:06:02,249 [Kristie]: So, so I've done a lot of things. 67 00:06:02,789 --> 00:06:06,144 [Kristie]: Right? I've taught English as a profession, 68 00:06:07,043 --> 00:06:08,103 [Kristie]: I was a professor. 70 00:06:11,478 --> 00:06:17,783 [Kristie]: I came to Domo, Domo made me an offer I couldn't refuse. Came to Domo about five years ago, which was a huge change 71 00:06:18,241 --> 00:06:23,864 [Kristie]: from academia. When you're in academia, you kind of believe that academia is the best home you could ever find. 72 00:06:24,284 --> 00:06:31,096 [Kristie]: And so leaving academia was really hard, and that was the most scary transition I've ever made in my whole entire life. 73 00:06:32,409 --> 00:06:37,363 [Kristie]: And I made it and it's so much fun. It's so much fun to be in tech. 74 00:06:37,923 --> 00:07:01,771 [Kristie]: I love it so much. So I don't know. I meander. I'm meander a lot, and I don't regret it. It's not very streamlined, but I've learned so much at every step along the way. And I don't know. If there are people out there who feel like they're meandering, I wouldn't necessarily discourage it. At some point, you do have to get a real job. And at some point, you do have to commit to something so you can propel yourself and be who you wanna be. 75 00:07:02,549 --> 00:07:04,306 [Kristie]: But it's okay to figure it out first. 76 00:07:08,039 --> 00:07:17,105 [Jason]: Well, I love that. And thank you, Kristie, for being part of our show today. Like, we are really honored to have you here with us and talk about your meandering. I think that's really good 77 00:07:17,722 --> 00:07:19,458 [Jason]: counsel too, to allow yourselves as 78 00:07:19,875 --> 00:07:29,285 [Jason]: people as you're exploring careers, and learning who you are and what really interests you, to allow that sort of meandering. That's a great, it's a great way of looking at it. 79 00:07:29,862 --> 00:07:30,694 [Jason]: And you've done a 80 00:07:31,712 --> 00:07:33,130 [Jason]: very effective job meandering 81 00:07:34,067 --> 00:07:35,445 [Jason]: through some really 82 00:07:35,823 --> 00:07:42,963 [Jason]: impressive jobs that anyone would love to land on. So that's good for you to continue like, to look until you found something, 83 00:07:43,301 --> 00:07:44,280 [Jason]: you know, at Domo. 84 00:07:45,097 --> 00:07:48,031 [Jason]: One of the things you said too that I found 85 00:07:48,463 --> 00:07:49,721 [Jason]: interesting. So your background, 86 00:07:50,138 --> 00:07:51,037 [Jason]: you know, piano 87 00:07:51,853 --> 00:07:52,712 [Jason]: guitar, music, 88 00:07:53,209 --> 00:07:54,188 [Jason]: one of my 89 00:07:54,645 --> 00:08:01,339 [Jason]: kinda hacks for finding who's a real data scientist is are they musically inclined? Because if they're not, 90 00:08:01,676 --> 00:08:08,334 [Jason]: then I start to question things because those parts of your brain from what I understand are like, so, so interconnected. So I wonder, 91 00:08:08,754 --> 00:08:15,794 [Jason]: I wonder if you had any thoughts on that, maybe tell us a little bit about how perhaps you feel music actually contributed to your career choice. 92 00:08:17,247 --> 00:08:18,864 [Kristie]: I could only speak for myself, 93 00:08:19,442 --> 00:08:29,492 [Kristie]: but for myself, I feel myself using the same part of my brain to write music as I do to come up with really creative mathematical solutions. 95 00:08:32,023 --> 00:08:34,535 [Kristie]: I don't know if that makes sense. Most of us don't feel ourselves think. 96 00:08:35,349 --> 00:08:39,565 [Kristie]: Maybe I think about it a little too much, but I feel myself using that same center 97 00:08:40,024 --> 00:08:42,482 [Kristie]: of my brain to come up with 98 00:08:44,873 --> 00:08:50,526 [Kristie]: scales and unique chording and things like that, as I do when I'm coding and writing the unique mathematical 99 00:08:51,958 --> 00:08:52,458 [Kristie]: solutions 101 00:08:54,511 --> 00:08:58,482 [Kristie]: that we come up with here at Domo. That's what we do, we do custom data science solutions. 102 00:08:59,672 --> 00:09:03,163 [Kristie]: So I feel myself using all of those same neural pathways 103 00:09:03,780 --> 00:09:06,274 [Kristie]: to do that. In terms of does 105 00:09:09,019 --> 00:09:12,988 [Kristie]: being musical make you a great data scientist? I don't know. It makes me a way better one. 106 00:09:13,485 --> 00:09:15,381 [Kristie]: It makes me a way better data scientist. 107 00:09:16,476 --> 00:09:22,654 [Kristie]: Because I can see how things piece together, not just in code blocks but how they piece together in bigger 108 00:09:23,071 --> 00:09:23,571 [Kristie]: stories. 109 00:09:24,068 --> 00:09:27,599 [Kristie]: And so it, it's absolutely made a difference in my career. 110 00:09:28,869 --> 00:09:30,009 [Kristie]: I think that it also 111 00:09:31,227 --> 00:09:40,327 [Kristie]: exercises a part of your brain that is nice to keep open and not necessarily pigeon hole yourself if you're in a technical career. Data science is really weird. It's not programming. 112 00:09:40,743 --> 00:09:41,800 [Kristie]: So if you're a programmer, 113 00:09:42,296 --> 00:09:45,027 [Kristie]: you probably can get away with just using 114 00:09:45,498 --> 00:09:53,692 [Kristie]: one or two or handful of neural pathways to get your your work done. And that's usually very effective for a lot of people. I feel like when you're 115 00:09:55,204 --> 00:09:56,884 [Kristie]: trying to create custom solutions, 116 00:09:57,364 --> 00:10:03,664 [Kristie]: you can't do that. You either have to hire people that fit each of those tasks or you have to be able to do it yourself. And I 117 00:10:04,100 --> 00:10:07,699 [Kristie]: am a do-it-yourself-er to a fault. I... This is not... I'm not bragging. 118 00:10:08,339 --> 00:10:09,759 [Kristie]: This is... I wish I 119 00:10:10,459 --> 00:10:12,059 [Kristie]: didn't do so many things myself. 120 00:10:12,873 --> 00:10:21,925 [Kristie]: But I do, I do. And part of that, I think does have to do with being able to piece things together, and just piece different aspects of your life together in a story. 121 00:10:23,378 --> 00:10:25,676 [Jason]: Well, I think what one of the things I say 122 00:10:26,095 --> 00:10:34,160 [Jason]: probably too often is that the dirty little secret of data science is that it's more of an art that a science. And I think that creative... 123 00:10:34,818 --> 00:10:40,746 [Jason]: What you're describing, that creative process and that methodology that you're developing and writing music, that 124 00:10:41,099 --> 00:10:46,184 [Jason]: probably does actually help train and think creatively to solve these, you know, these custom 125 00:10:47,080 --> 00:10:54,620 [Jason]: data science solutions that you're putting together for your customers. I think, I think it probably does directly help. I agree with everything you're saying there. And I think 126 00:10:55,798 --> 00:10:59,155 [Jason]: one of the other questions I have too is, I mean you kinda touched on it. 127 00:11:00,050 --> 00:11:01,550 [Jason]: Being very hands on 128 00:11:02,089 --> 00:11:16,235 [Jason]: through your career, you know, at least over the history that I've gotten to know you, you have an extremely technical role, but also a very strategic role. And you're sort of straddling that back and forth between having to get in and be very strategic, 129 00:11:16,869 --> 00:11:35,279 [Jason]: and then implement and do things super technical. And so I'm wondering if you could maybe just talk about some of the, some of the methods or skills you use to sort of, like, bridge that gap to kinda level up and level down sort of as rapidly as you have to, probably oftentimes within the same day or even hour. 130 00:11:36,775 --> 00:11:40,708 [Kristie]: You are right. I don't think about that very often. So I'm glad you pointed it out. 131 00:11:41,645 --> 00:11:42,145 [Kristie]: Interestingly, 132 00:11:42,483 --> 00:11:46,354 [Kristie]: we just took the Clifton strengths assessment as a team 133 00:11:46,854 --> 00:12:08,006 [Kristie]: and strategic was my number one. It was my number one thing, and I love strategy, and I've always known that. I love it. If I didn't have it my life, I'd probably wither and die. So I would not enjoy the data science role, and getting down in the weeds and doing all of the technical things that being a data scientist requires, if I couldn't be strategic. So 134 00:12:08,917 --> 00:12:10,373 [Kristie]: I kind of do those 135 00:12:10,749 --> 00:12:15,570 [Kristie]: technical down in the weeds things because I can. And I'm proud of the fact that I can do them. 136 00:12:16,264 --> 00:12:22,544 [Kristie]: But what I really enjoy is the strategy. I don't know that I ever get away from the strategy. It's always on my mind. 137 00:12:23,184 --> 00:12:27,717 [Kristie]: My mind is always playing Tetris. Like, I'm always seeing the pieces fall and I... you know, 138 00:12:28,516 --> 00:12:29,016 [Kristie]: sometimes 139 00:12:29,395 --> 00:12:44,157 [Kristie]: ten or eleven pieces that you haven't seen yet. I know where they're going to land, and how that's going to work out. And if I don't get the piece I want, I can shift things around and make it work. So I always have backup plans, and backup plans to those backup plans, because I'm always thinking about strategy. 140 00:12:45,866 --> 00:12:50,176 [Kristie]: So I like it. And I don't think I move in and out of it. I think I always think about strategy. 141 00:12:50,735 --> 00:12:52,930 [Kristie]: And I force myself to take breaks from it. 142 00:12:54,022 --> 00:12:59,906 [Kristie]: Because if you think about strategy too much, it's really, really hard to ground yourself into 143 00:13:00,243 --> 00:13:01,980 [Kristie]: what's important right now in the moment. 144 00:13:02,636 --> 00:13:03,136 [Kristie]: So I 145 00:13:03,529 --> 00:13:06,386 [Kristie]: feel like I have to take scheduled breaks from being strategic. 146 00:13:07,444 --> 00:13:09,581 [Kristie]: So hopefully, that answers your question. 147 00:13:10,734 --> 00:13:11,674 [Jason]: I like that, 148 00:13:12,334 --> 00:13:14,354 [Jason]: that's interesting. You need to give you sort of, like, 149 00:13:14,694 --> 00:13:23,262 [Jason]: that rest or whatever to let yourself reset and take out of it. When you're taking those breaks from being strategic, is it taking a break from being strategic and coding? 150 00:13:23,781 --> 00:13:25,790 [Jason]: Or is it actual rest? 151 00:13:26,627 --> 00:13:27,286 [Kristie]: Nope. Yeah, not really. 152 00:13:27,702 --> 00:13:31,959 [Kristie]: Not really. It's just kind of retooling that strategy to get a different task done. 153 00:13:32,538 --> 00:13:35,869 [Kristie]: Thanks for pointing that out. I'm less effective at this than I thought... 154 00:13:38,161 --> 00:13:41,173 [Jason]: Something, something tells me you're still highly effective at this. 155 00:13:42,204 --> 00:13:43,384 [Jason]: I think, you know, kind of 156 00:13:44,404 --> 00:13:48,944 [Jason]: extending maybe that a little bit further and circling back also to some of the things you talked about. 157 00:13:50,138 --> 00:13:55,818 [Jason]: Throughout your career, especially, you know, being a woman in a technical field, that mentorship 158 00:13:56,155 --> 00:13:57,652 [Jason]: component. And, like, how 159 00:13:57,964 --> 00:14:01,624 [Jason]: like, the advice that you got or maybe didn't get along the way. 160 00:14:02,484 --> 00:14:10,697 [Jason]: And I think you've shared a few pieces of, I would say, wisdom for folks. But are there pieces of specific advice, you would look to give someone, 161 00:14:11,313 --> 00:14:14,384 [Jason]: you know, from a generalized mentoring perspective, 162 00:14:14,924 --> 00:14:20,324 [Jason]: for folks who are looking to their grow professionally in this space or perhaps make a career move into the space? 163 00:14:21,655 --> 00:14:25,940 [Kristie]: I have so many different ideas on that. I'll hopefully cover a couple. 164 00:14:26,436 --> 00:14:26,936 [Kristie]: And 165 00:14:27,313 --> 00:14:31,464 [Kristie]: the first one, and it's something that we've talked about a little bit. I had 166 00:14:32,084 --> 00:14:33,644 [Kristie]: a seventh grade teacher 167 00:14:34,044 --> 00:14:35,204 [Kristie]: named Mrs. Geery, 168 00:14:35,884 --> 00:14:36,944 [Kristie]: and she just... 169 00:14:37,659 --> 00:14:51,692 [Kristie]: She saw that I was talented mathematically, and she believed in me, and she's like this girl needs to be in advanced match. She has to be in advanced math classes, they happen this year. She has to be in it, she's really talented. And I was like, yeah I'm gonna be a lawyer, 170 00:14:52,967 --> 00:14:53,325 [Kristie]: who cares? 171 00:14:54,419 --> 00:14:56,599 [Kristie]: But she really believed in me 172 00:14:57,379 --> 00:15:08,966 [Kristie]: and she believed to me so much that she called my parents in. She called me and she called the principal in. And she made sure that that happened. And everybody needs a Mrs. Geery. And I didn't appreciate that nearly as much as a seventh grader. 173 00:15:09,585 --> 00:15:20,404 [Kristie]: But I certainly did when I decided not to be a lawyer and decided to go into research. I knew that I had talent in that area and somebody believed in me at some point in my life. 174 00:15:20,824 --> 00:15:26,124 [Kristie]: And that was ini seventh grade. And you're thinking, what does a seventh grade math teacher have to do, you know, when you're 175 00:15:26,439 --> 00:15:29,650 [Kristie]: 22 years old and you're going to get a PhD? How does that happen? 176 00:15:30,147 --> 00:15:35,210 [Kristie]: I can't explain it. It just does. Having somebody believing in you at some point in your life sticks with you for a really long time. 177 00:15:36,142 --> 00:15:38,117 [Kristie]: So one of the things that I would say 178 00:15:38,494 --> 00:15:42,302 [Kristie]: is, find mentors that believe in you. If you, and if you, if you don't have that... 179 00:15:44,647 --> 00:16:03,114 [Kristie]: Everybody needs a Mrs. Geery. Like you have to have somebody who believes in you. And not just believes in you because, hey, you're a great person, I believe in you because you're great and you're my friend. But believes in your skills and your ability to grow your skill set and your brain's ability to be flexible enough to learn and grow into roles that you care about. 180 00:16:04,692 --> 00:16:06,170 [Kristie]: So I think that would be the first one. 181 00:16:07,063 --> 00:16:14,582 [Jason]: I would, I would say Kristie on that too, for folks as you're thinking about that, that applies to both sides of the equation. And making sure that if 182 00:16:14,999 --> 00:16:16,416 [Jason]: we, as leaders, 183 00:16:16,729 --> 00:16:24,500 [Jason]: see people that we believe in, to make sure we communicate to them. Because I think there is probably a little bit of a lack of that communication of, like, knowing 184 00:16:24,854 --> 00:16:39,525 [Jason]: how to express when you see people that are amazing, and being able to articulate and be that person for them who may not have had that in the past. So it's sort of like not assuming that people have been built up. Right? So taking the time as leaders to make sure that we recognize people. 185 00:16:40,660 --> 00:16:43,512 [Kristie]: I think that's a really good point. And to do it deliberately, 186 00:16:44,303 --> 00:17:04,934 [Kristie]: with them in the room, you know? I know that I champion my people, I have the best team ever. They're so amazing and I champion them all the time. But I don't know if they're always in the room while I'm championing them. And to give them that feedback, and let them know that that's how I feel about them. I think it's a really critical piece. So I I love that. I should do better. Thank you. 187 00:17:06,475 --> 00:17:07,855 [Jason]: You know you're awesome, right, Anna? 188 00:17:09,130 --> 00:17:11,909 [Jason]: I'm pretty sure I consistently tell you that. 189 00:17:15,570 --> 00:17:19,600 [Jason]: And do you have anything else on the mentorship front? I feel like I may have cut you off a bit. 190 00:17:20,378 --> 00:17:22,636 [Kristie]: I think the other thing that 191 00:17:23,470 --> 00:17:30,750 [Kristie]: I would wanna say to anybody who's pursuing a technical role, especially in something like data science work, I think impostor syndrome runs rampant. 193 00:17:32,445 --> 00:17:34,225 [Kristie]: It's just rampant. 194 00:17:35,485 --> 00:17:41,395 [Kristie]: Nobody feels like they're good enough, and everyone kind of feels like they need to peacock a little bit and always have all the answers. 195 00:17:41,972 --> 00:17:44,827 [Kristie]: And I rarely find that that approach is helpful. 196 00:17:45,645 --> 00:17:47,941 [Kristie]: And I think unfortunately, sometimes 197 00:17:49,892 --> 00:17:56,026 [Kristie]: women experience the impostor syndrome even more and sometimes... it goes in one of two directions. It's either debilitating 198 00:17:57,057 --> 00:17:59,629 [Kristie]: and you never get to where you, where your 199 00:18:00,484 --> 00:18:00,984 [Kristie]: potential lies, or 200 00:18:01,838 --> 00:18:03,932 [Kristie]: it goes the opposite direction and 201 00:18:05,122 --> 00:18:10,905 [Kristie]: you over commit, and oversell your abilities in ways that is off putting to a lot of people. 202 00:18:11,399 --> 00:18:21,674 [Kristie]: And I find that just being really honest about who you are, and what you know, and being willing to learn and grow is almost always the best approach. In fact, when I interview people, 203 00:18:22,414 --> 00:18:23,394 [Kristie]: one of my goals 204 00:18:23,854 --> 00:18:27,094 [Kristie]: is to ask them questions until they say that they don't know the answer. 205 00:18:27,748 --> 00:18:30,257 [Kristie]: And if they don't... If they never say I don't know, 206 00:18:30,974 --> 00:18:32,231 [Kristie]: then I don't hire them. 207 00:18:32,727 --> 00:18:33,227 [Kristie]: So 208 00:18:34,653 --> 00:18:41,305 [Kristie]: they need to be humble enough to admit when they don't know something, and also know how to deal with stuff when they don't know the answer. 209 00:18:42,118 --> 00:18:49,500 [Kristie]: You're not always gonna know the answer. Like, my team is pushing the boundaries of what data science can do and what machine learning is capable of all the time. 210 00:18:49,994 --> 00:18:54,468 [Kristie]: We always don't know. I spend... I spend my time doing something every day that I don't know. 211 00:18:55,147 --> 00:18:56,505 [Kristie]: And if we can't admit that, 212 00:18:56,999 --> 00:19:01,694 [Kristie]: then I think it undermines our whole reason for pursuing and growing in the way that we have. 213 00:19:02,832 --> 00:19:05,144 [Jason]: Can you maybe talk a little, I think 214 00:19:05,683 --> 00:19:10,569 [Jason]: part of being able to be humble in that regard is allowing yourself to be vulnerable 215 00:19:10,946 --> 00:19:14,415 [Jason]: in that regard, giving in that requires a tremendous amount of confidence. 216 00:19:14,908 --> 00:19:20,397 [Jason]: And so I think I mean, I'd be curious to know like, how would you go about 217 00:19:20,895 --> 00:19:24,284 [Jason]: helping someone develop that confidence who may, 218 00:19:25,304 --> 00:19:36,567 [Jason]: you know, even potentially be at a disadvantage because they feel like they may be being questioned, and they may feel like they need to just exude confidence even when they don't have it. So, like, can you maybe talk through a little bit about that? 219 00:19:37,463 --> 00:19:39,714 [Kristie]: One of the things that I like to do, 220 00:19:40,074 --> 00:19:45,134 [Kristie]: especially with my brand new data scientists, is to surround them with amazing people. 221 00:19:46,354 --> 00:19:46,854 [Kristie]: And 222 00:19:47,289 --> 00:19:58,833 [Kristie]: the reason that I think that's important is because, when they get on a call with a customer, when they're interacting with key business stakeholders, where we need to be confident and we need to exude that confidence, 223 00:19:59,329 --> 00:20:07,189 [Kristie]: they can kinda lean on their team a little bit. It's a little bit of a crutch, so they almost don't have to be super confident in themselves. They can be confident in 224 00:20:07,689 --> 00:20:09,269 [Kristie]: themselves as being part of a group. 225 00:20:09,729 --> 00:20:11,884 [Kristie]: And then they start to take on that 226 00:20:12,343 --> 00:20:12,843 [Kristie]: identity 227 00:20:13,300 --> 00:20:14,996 [Kristie]: of being very, very capable. 228 00:20:15,612 --> 00:20:27,489 [Kristie]: And then we can't, I can't keep them there either. Right? You do that as a stepping stone. But ultimately, you have to get to the next step to where they're taking on assignments all by themselves and getting a lot of good feedback on that. 229 00:20:28,143 --> 00:20:30,935 [Kristie]: And I think that that's one place where leaders can fall down a little bit. 230 00:20:31,972 --> 00:20:40,309 [Kristie]: When when I notice someone having confidence issues and it's time for them to have their own confidence on their own, one of the best things I can do is give them 231 00:20:40,729 --> 00:20:43,469 [Kristie]: projects that they have to deliver on their own. 232 00:20:44,049 --> 00:20:50,619 [Kristie]: And not to abandon them. Like, let's work on it together. Let's let's do it together, but I wanna see what you can do. 233 00:20:51,219 --> 00:21:02,450 [Kristie]: And then let's set you up for a really, really good success story when you go deliver this presentation, or where you go to this customer meeting, or you're engaged in the rollout of a new solution. 234 00:21:03,422 --> 00:21:11,395 [Kristie]: So giving people responsibility and then helping them be successful in that responsibility out of the gate, I think is really key at helping develop confidence. 235 00:21:11,969 --> 00:21:16,009 [Kristie]: And understanding that it's not something that's gonna happen overnight. It takes years and years to develop. 236 00:21:19,504 --> 00:21:20,564 [Jason]: Yeah. I think that's 237 00:21:21,864 --> 00:21:29,296 [Jason]: trying to shorten that cycle of years and years to develop. I think what you're talking about there is just ways to help bring that confidence to a person sooner. 238 00:21:29,874 --> 00:21:37,544 [Jason]: I love that, I like the autonomy that you're giving people too, to work through those projects where they're, but still surrounded by all that strength. I think that's 239 00:21:37,884 --> 00:21:38,864 [Jason]: that's fantastic. 240 00:21:40,724 --> 00:21:41,424 [Jason]: I have 241 00:21:42,219 --> 00:21:44,519 [Jason]: perhaps a little bit more of a technical 242 00:21:44,939 --> 00:21:49,199 [Jason]: question. So one of the things that we often run into is, 243 00:21:49,539 --> 00:21:52,330 [Jason]: you know, how do we prove what we're doing is working? 244 00:21:52,747 --> 00:21:57,636 [Jason]: Right? So, like, prove that this is working. And so we're constantly, like, kind of in this, sort of, like 245 00:21:58,108 --> 00:22:09,004 [Jason]: defending storytelling type of a mode when it comes to data science applications or even kind of more broader, broader things in the business analytics world. So I guess I would say like, perhaps, in your experience. 246 00:22:09,344 --> 00:22:14,644 [Jason]: What are some of the challenging or most challenging aspects of that? Maybe how are you addressing those? 247 00:22:16,959 --> 00:22:17,899 [Kristie]: That's a good question. 248 00:22:20,919 --> 00:22:21,979 [Kristie]: Just a fundamental 249 00:22:22,439 --> 00:22:25,139 [Kristie]: flaw in my personality is that I never 250 00:22:25,454 --> 00:22:29,685 [Kristie]: think that I've done anything good enough ever. I never think that what I've done is good enough. 251 00:22:30,923 --> 00:22:31,423 [Kristie]: And 252 00:22:32,200 --> 00:22:38,463 [Kristie]: I'm really trying to fix that because that's not a good attribute of a leader to have, if it rubs off on their team. 253 00:22:39,400 --> 00:22:44,611 [Kristie]: The team needs to feel successful and the team needs to know when they've had definitive successes 254 00:22:45,803 --> 00:22:52,595 [Kristie]: but I myself. I... I'm always looking for different ways to do stuff. So I have the opposite problem of figuring out where we've won. 255 00:22:53,529 --> 00:22:54,789 [Kristie]: And where we can do better. 256 00:22:55,449 --> 00:23:04,644 [Kristie]: One of the things that we always do after we wrap up a project is, we sit down together as a team and we talk about what worked, what didn't work what would we do differently next time? 257 00:23:05,504 --> 00:23:07,104 [Kristie]: What would we like to do in the future? 258 00:23:07,904 --> 00:23:10,684 [Kristie]: And it's a really constructive conversation 259 00:23:11,104 --> 00:23:11,604 [Kristie]: about, 260 00:23:12,758 --> 00:23:16,295 [Kristie]: if the team were set loose to do whatever they wanted and maybe weren't 261 00:23:18,072 --> 00:23:25,464 [Kristie]: restricted by certain criteria of a project or scopes of projects or whatever it may be. We get to talk about 262 00:23:26,084 --> 00:23:32,919 [Kristie]: what they want to do. And how they want to do it. And they have so many amazing ideas, way more amazing ideas than I have on my own. 263 00:23:34,099 --> 00:23:34,534 [Kristie]: And 264 00:23:34,913 --> 00:23:36,110 [Kristie]: and they're really... 265 00:23:36,629 --> 00:23:47,915 [Kristie]: We also have a culture on my team where, no. We seriously want to know what we did wrong, we really wanna talk about it. Not because you're gonna get fired. Not because there's any real high stakes about being wrong. 266 00:23:48,412 --> 00:23:58,474 [Kristie]: But we always want to get better and we... If we love the thing, then we want it to be better. And the only way we can do that is if we talk about the things that we didn't do great. So 267 00:23:58,814 --> 00:24:00,374 [Kristie]: it's part of our culture to do that. 268 00:24:01,229 --> 00:24:06,369 [Kristie]: But I also think it's really important to just let go. And I don't let go very easily, 269 00:24:07,469 --> 00:24:13,380 [Kristie]: but I need to because you get so many amazing ideas from your people who are also 270 00:24:13,719 --> 00:24:15,398 [Kristie]: amazing humans in so many ways. 271 00:24:16,277 --> 00:24:17,976 [Kristie]: And it's been just really 272 00:24:19,689 --> 00:24:25,509 [Kristie]: humbling for me to learn from all of the people that I work with. I say all the time that my 273 00:24:25,849 --> 00:24:31,438 [Kristie]: my friend group and my teams at Domo, have been the very best, most talented, brightest, 274 00:24:32,055 --> 00:24:39,354 [Kristie]: impressive people I've ever met in my entire life. And it is amazing to be around them. And to see what kind of synergy happens when you let them 275 00:24:40,012 --> 00:24:44,583 [Kristie]: talk about where they see their failures, and where they see their strengths, and how they want to grow. 276 00:24:47,411 --> 00:24:53,195 [Jason]: Got it. I mean, I think yeah. It's it's... A lot of what you're saying too really comes back to the team that you're with and that you're building, and 277 00:24:53,649 --> 00:24:55,827 [Jason]: you're a part of. So I think that's really good. 278 00:24:56,726 --> 00:25:05,186 [Jason]: That makes a lot of sense and I think too, one of the things you said, you never feel like your work is good enough, and I'll just share from from what I see. I'm constantly impressed and amazed by what you're doing. So 279 00:25:05,523 --> 00:25:09,968 [Jason]: from from my opinion, it's beyond good enough, from what I'm seeing. It's amazing. 280 00:25:10,919 --> 00:25:17,285 [Jason]: Kind of along those lines, I have sort of one final question, that I'll put out there for you. Can you... 281 00:25:17,942 --> 00:25:18,860 [Jason]: So you work at Domo. 282 00:25:19,234 --> 00:25:22,494 [Jason]: Can you share something that you love about Domo that 283 00:25:22,834 --> 00:25:25,214 [Jason]: folks like myself who don't work there, wouldn't know. 284 00:25:30,561 --> 00:25:31,319 [Kristie]: To me, 285 00:25:32,277 --> 00:25:34,334 [Kristie]: Domo and being at Domo, it's 286 00:25:36,683 --> 00:25:37,940 [Kristie]: It's almost a feeling. 287 00:25:38,358 --> 00:25:41,609 [Kristie]: It's an attitude and it's a feeling. And it's about 288 00:25:42,066 --> 00:25:46,129 [Kristie]: doing our best work and being the most creative that we can possibly be 289 00:25:46,868 --> 00:25:47,368 [Kristie]: to 290 00:25:48,666 --> 00:25:49,765 [Kristie]: be there for our customers, 291 00:25:50,384 --> 00:25:53,894 [Kristie]: to give them the very best experience we can possibly give them 292 00:25:54,234 --> 00:25:57,709 [Kristie]: and that rubs off on the experience of the employees too. 293 00:25:58,548 --> 00:26:01,005 [Kristie]: It has been my extreme pleasure 294 00:26:01,799 --> 00:26:04,776 [Kristie]: to work at Domo and to be able to hire 295 00:26:05,275 --> 00:26:07,453 [Kristie]: really talented amazing women 296 00:26:07,992 --> 00:26:10,869 [Kristie]: and talk to them about what maternity leave looks like at Domo. 297 00:26:11,364 --> 00:26:14,344 [Kristie]: And how family friendly demo is just in general. 298 00:26:14,684 --> 00:26:21,999 [Kristie]: And so all of that effort that we pour into our customers, and it's not because we come in from 9 to 5 and check, we did our job. 299 00:26:22,619 --> 00:26:26,934 [Kristie]: It's because we love our customers. And that that love of people rubs off 300 00:26:27,274 --> 00:26:32,224 [Kristie]: on everybody at Domo, and we care about each other in ways that I haven't seen at other companies. 301 00:26:33,262 --> 00:26:36,216 [Kristie]: So that's the thing that I think I really love the most about Domo. 302 00:26:37,827 --> 00:26:38,606 [Jason]: That's great. 303 00:26:39,224 --> 00:26:40,482 [Anna]: That's wonderful, Kristie. 304 00:26:40,820 --> 00:26:52,604 [Anna]: And thank you so much for joining the podcast today and for giving us your time, your advice, giving us some of your stories, some of the things you love about the company you work at and kind of how you got there, 305 00:26:52,919 --> 00:26:59,972 [Anna]: and a lot of the advice that you've shared for someone who may looking to get into this space or advance in this space. I think it's really 306 00:27:00,591 --> 00:27:09,809 [Anna]: important for us to hear from know, people in positions of power that you know, you really care and you really do look to develop that in other people. So thank you so much for sharing that 307 00:27:10,187 --> 00:27:11,264 [Anna]: and for your time. 308 00:27:11,718 --> 00:27:15,866 [Anna]: Is there anything else that you want to leave our listeners with today before we sign off? 309 00:27:18,179 --> 00:27:21,071 [Kristie]: This has been a pleasure. Thank you so much for having me. 310 00:27:21,504 --> 00:27:24,158 [Kristie]: I I hope I've said some valuable things too. 311 00:27:25,255 --> 00:27:39,823 [Kristie]: I think the only thing I would say is that if we're talking to anybody out there who's pursuing a technical role, and maybe thinks that it's not for them or maybe thinks that they can't do it or don't feel like they've gotten the right support to get to where they're going. 312 00:27:41,440 --> 00:27:44,117 [Kristie]: I would say if you're passionate about it, stick with it. 313 00:27:45,076 --> 00:27:47,095 [Kristie]: There are people out there who will help you. 314 00:27:47,529 --> 00:27:50,125 [Kristie]: There are people out there who will help you navigate it. 315 00:27:51,164 --> 00:27:54,460 [Kristie]: It can be challenging, but I think it's really rewarding. 316 00:27:55,893 --> 00:27:57,472 [Kristie]: And that you have to start 317 00:27:58,410 --> 00:28:07,572 [Kristie]: often in places that you don't wanna start at. You often don't start in the job that you want. You start in the job that is going to get you to where you want to be in five years. 318 00:28:08,450 --> 00:28:12,360 [Kristie]: And I think that today people get out of school and they often 319 00:28:12,814 --> 00:28:15,432 [Kristie]: expect to be in the job that they want to be in 320 00:28:15,891 --> 00:28:30,760 [Kristie]: in the next five to ten years, and that's often not what's available and that's often not where you start. And it's okay. It's okay. Just take every opportunity that you get and turn it into the thing that you want. I did not actually come to Domo as a data scientist. That's absolutely what I am, 321 00:28:31,373 --> 00:28:33,149 [Kristie]: but I came to Domo 322 00:28:34,205 --> 00:28:36,858 [Kristie]: in a completely different role because I saw potential, 323 00:28:37,236 --> 00:28:39,411 [Kristie]: and there were no data scientists at Domo when I got there. 324 00:28:39,748 --> 00:28:40,966 [Kristie]: So so 325 00:28:41,479 --> 00:28:41,979 [Kristie]: the... 326 00:28:42,597 --> 00:28:46,294 [Kristie]: There was no data science consulting. There really was no data science part of the product. 327 00:28:46,713 --> 00:28:47,452 [Kristie]: And so 328 00:28:47,831 --> 00:28:51,942 [Kristie]: take the opportunities that are in front of you, and make them what you need them to be 329 00:28:52,640 --> 00:28:55,816 [Kristie]: to grow into the person and the professional that you want to be. 330 00:28:57,686 --> 00:29:03,447 [Anna]: The Real Intelligence podcast is presented by RXA, a leading data science consulting company. 331 00:29:03,864 --> 00:29:09,454 [Anna]: RXA provides project-based consulting, staff augmentation, and direct hire staffing services 332 00:29:09,754 --> 00:29:10,734 [Anna]: for data science, 333 00:29:11,074 --> 00:29:13,214 [Anna]: data engineering and business intelligence 334 00:29:13,609 --> 00:29:16,546 [Anna]: to help our clients unlock the value in their data faster. 335 00:29:17,045 --> 00:29:27,629 [Anna]: Learn more by visiting our website at www.rxa.io or contacting our team at [email protected] today.