Real Stories from a Panel of Successful Career Switchers | Kaggle

okay we're gonna go ahead and get started I'm really excited to kick off our last livestream aircon session and it's titled real stories from a panel of successful career switchers so in this session we've asked three working data scientists to candidly share their backgrounds and unique journeys to data science each of the panel today have been featured in career track in infographics on our blog so if you haven't seen those yet I encourage you to go to no free hunch at blog keggle comm and check out their journeys and if you have any kind of specific questions for any of the panelists feel free to share them on youtube live or in our slot Q&A Channel and we'll get those asked at the end of the session and so I'd like to start off and let the panelists introduce themselves and talk about what they're doing now and their current role and kind of what that looks like on the day-to-day so why don't we start with you yes my name is Jesse master Peck and I am the director of data systems and analysis for teaching trust we are an education management nonprofit here in Dallas Texas and we really look to develop school leaders as a way to improve student academic outcomes and my day is kind of a 50/50 split between doing actual data science and then also focusing a lot on strategic planning I spend a lot of time looking at what does it look like to build a data science team how do we build a data-driven culture and then how do we kind of build best practices for data science within a nonprofit organization Megan hey everybody I'm Megan reust all and I'm a data scientist and product lead at cattle I work on data sets our public data platform and I do community community advocacy more broadly I work out of the Google office in Los Angeles where it's usually very sunny but today extremely rainy so kind of in my day to day I get to wear many hats and kind of how things break down is I do a lot of building awareness of Kyle's platform and tools among data scientists and I do things like develop content technical content tutorials demos on our platform work with other people internally to develop this type of content and then I also act as a liaison between our community and our engineers and designers and then more recently where it's really exciting to be part of this panel talking about career queer career switching is I feel like I'm really still evolving in my career switch lately I've assumed more product leadership roles focusing on specifically on our public data platform having an opportunity of work really closely with our engineers and then I guess I'd say like although what I do isn't necessarily what a typical data scientist does in their day to day a common theme for me is being able to understand and advocate what data scientists do communicate that and have an influence on how they use our product and David yeah thanks Anna so my name is David O'Hara I'm really excited to be here today I am in my first data scientist role I work for GE aviation and I'm based out of Cincinnati Ohio the business unit that I work in has about a hundred and fifty data scientist globally with about half that amount in the US and the group looks to you know about a fourth of it looks externally to spell solutions to our aircraft engine customers and then the remainder looks internally to help with productivity projects so I'm on the internal facing side where I work with the various shops that we have across the world to figure out ways to make aircraft engine parts cheaper and more efficiently so it's it's really fun it's um I work a lot with engineers so I get I get to learn how the the aircraft engine operates and but I also work with a lot of different functions I work with the supply chain I work with RIT leadership I work with our commercial team so I've been with the business for about 15 years primarily in finance so it's something different also since data sign science is a newer field I consider myself an ambassador and helping establish this field in aviation thanks hey and we're really excited at just how diverse our panel is in terms of kind of what you do on the day-to-day and what types of companies you work for it though we're excited to talk about more about that later but I mean to really get into kind of this career switching aspect which is why we're all here I'd love to hear about what other career options kind of pre data science you explored and you know ultimately why you decided to move away from them that may be why they weren't a good fit so Megan do you want to start sure I can start so um I think the first part of your question was kind of what where was i before data science and I guess going back to like the things that I highlighted in my my winding path the data science is my infographic on the blog really I thought I was going to be a musician performance artist you know this is in high school I played oboe for you know over a decade I went to a music conservatory played oboe thought I was gonna you know this was it I was gonna be a musician and I ended up getting a repetitive stress injury had just sort of like have you know have this moment of self-reflection they decide what I want to do so I ended up studying social sciences I got a bachelor's in psychology from a State College in Wisconsin and I also studied French like I'm interested in in languages that took me to studying linguistics quantitative linguistics at NC State and this is really kind of you know my educational path as where I sort of fell in love with research working with data and you know at that point you know when I was doing a master's degree in linguistics I really felt like okay I'm gonna be a you know I'm gonna stay in the academic Academy I'm gonna become a researcher I'm gonna stay in linguistics write papers become a professor get tenure all of those things and I sort of felt like okay this is my trajectory so I decided to do a PhD in linguistics at UCLA and then a year into it I sort of you know started to feel a little bit disillusioned with academia and felt like I was very personally excited and working with data and using data to answer really research questions that I felt were really interesting but I could you know academia felt really closed off to me and you know those stuff I was working on was interesting to like highly interesting a really small group of people so I explored applying fir'd for jobs outside of you know grad school and I ended up getting an interview at Google that's really what that was like the moment for me that I realized like okay I can do this I don't need to stay in academia I didn't end up getting the job but it was it's what gave me the confidence to know that I had skills that I could fly outside of research and David how about you yeah so you know ironically I grew up in a really small town and in the middle of Illinois a farming community so if anyone can make it if I made it in data science anyone can make it in high school we had a keyboarding class which I didn't do well because I couldn't type fast enough so humble beginnings but anyway I started my career in finance because I always liked numbers and I always liked to read and research personal finance on the side so it kind of fit with ultimately when I made that change the data science because I feel like I could read really books every day or research some kernel and uncle it's it's it's addicting but it's addicting in a good way but ultimately my career 13 14 years in corporate finance I I decided to change it because it really wasn't my passion anymore for me I'm a passionate person I'm full of energy and I need to have that fire when I go to work and the journey for me changed when my daughter she's four now when she was two she got diagnosed rare form of leukemia and it was just you know it was one of those life-altering experiences that you wouldn't wish on your worst enemy but we're we're all stronger as a result but the the medicine that she took the chemotherapy was 50 years old so there's a lot better treatments that are coming out I always made argument with my wife that there's got to be data out there where we can figure out a way to help these sick kids and that kind of one thing led to another and it led me to Kegel and data science and I've made the jump that I'm here you're now talking to you guys but ultimately my goal is to help with pediatric cancer research and I would do that with the skills that I learned at work and transfer it to my hobby outside of work it's such a touching story obviously and it's also I mean I love when Cal hosts competitions and medical research I'm sure you do too and I know there's a lot of people who've been on our open data platform that you probably have common interests oh yeah and John yeah so similar to Megan and I started out in academia I went from it took me eight years to finish an undergrad and then graduated into the 2008 recession which is about as much fun as everybody tells you it is so went to grad school loved working in the lab and I didn't listen to anyone's advice I was the first person in my family to go to college definitely the first photograph ool and everybody tells you choose the professor not the project and I was like it's totally fine like I've got this and I choose a project that I loved I was studying the neurological transmission of the infectious prion protein which is responsible for things like mad cow disease I was really interested in moving into neurodegenerative diseases like Parkinson's MS things like that as kind of a career in academia but I just after three years of running the same Western blot over and over and over and still getting the same results and having an advisor who said well it's still you it's not it's not the research I was just like it can't be me there was so it was it was kind of this moment of self-reflection where there was no course based masters there was no Department masters so it was either leave with nothing or stick it out for for as long as it took so I did leave and I did you know as mark kindly pointed out in one of our interviews I left with Jabar I took a statistics course I learned how to use R and that probably is where all of this kind of started and took hold I moved to New York City and I taught high school science so I transitioned from kind of studying science to teaching science and I absolutely loved it I loved teaching I loved teaching high school I loved teaching high school science my first data science project was actually looking at tests New York State tests and then pulling out keywords and doing a text analysis and then building my curriculum on the most common words and I just loved it I thought it was the most fan it felt like a calling it's the first thing I think I ever did where I was like this is what I meant to do and it just I over worked myself I was working six days a week I was putting in an 80 hour work week I had really poor time management skills and I just I burned out really really fast so it was just yeah that was kind of where I ended up taking some time off to really think about what I like doing and what I was capable of doing and it was around the time that data science was kind of building momentum as a career choice and it was definitely a moment of timing where I thought you know I can do that I can definitely make that leap and I was fortunate to be able to do so great so we have a question coming in that I was already planning back so it's perfect um which is just you know what skills best prepared you or what roles best prepared you for the work that you're now doing as a data scientist so whether they're soft skills or you know hard skills but what have you been able to really use from seemingly unrelated roles in your roles now um David you wanna start yeah sure sorry it took me a minute on the B button yeah absolutely I think that's a great question and after reading and researching you know extensively as I entered this field I think what I walked away with was that I needed to be an expert statistician which I do not love statistics I get through it and also the best coder out there in the world but really realistically what I found is those skills are important but it's much more important to have kind of a softer side of things so the role that I think prepared me the most was a role when I was the finance manager for an acquisition site in England and you know I was responsible for making sure that the site grew and and recorded the 215 million dollars of revenue properly and in that role everything kind of went sideways and after the first audit and I really had to rebuild the accounting structure but to do that I had to take difficult concepts and communicate these accounting concepts to non-technical people and to me that's the heart of data science if you can show people how you translate a huge amount of Tetra byte of data to a couple actionable results then you can have them rally behind you and you can make an impact with data science and I think in addition to that in that role I learned how to execute and also be responsible and take actions from my responsibility so that's that's me in a nutshell Thanks yeah mine's very similar to what David said it's the ability to communicate knowledge and so for me it was teaching so how do I take this big complex topic of biology or chemistry and how do I pull out what is the core idea that you need to know and then how do we use that and break it down into component pieces so that kind of the end-use are in many cases students but now co-workers and colleagues and and the broader community how do they then take ownership of this knowledge and build the foundational knowledge and then build levels up and build kind of on this core concept so it's very much centered around being able to help other people be able to communicate and help other people understand really what it is that you're trying to accomplish with data science great yeah I definitely agree both Jesse and David and I think what I'd add to that you know from my experience I I had a lot of background in statistics through grad school and I think you know like learning those fundamentals is really important but one thing that really stood out to me was just the power of data visualization in communication I think that's something that um you know has really served me well sort of translating from academia to to more of a data science role where I have to you know communicate ideas from data with other people add a visualization done well is really powerful and then another thing that I would add is being able to you know ask the right question and then being able to figure out you know what kind of tools and what cut what do I need to do to be able to answer this and then how do I take that answer and communicate it that's something that I learned from research as well that I say translates to data science definitely so we have a follow-up question just that I think what's kind of sparked by something you said which is you know in talking about your experience and research and so you know how does this kind of every person for themselves attitude that you can see in academia alongside some kind of you know lack of positive reinforcement compared to the work environment that you have in your companies right now in data science yeah so I work at an education organization which means I'm pretty much surrounded by teachers so it's probably the most supportive environment I've ever been and with people who really really focus on high quality communication skills and I don't know that that is you know it's nothing like I've ever experienced I think it is very unusual to be surrounded by teachers and and collaborating with teachers all the times within data science and looking at some of my other positions though I don't think I've ever been anywhere that is as dog-eat-dog as academia I've had a lot more support in every other organization that I've I've worked in beyond academia and and some of that has to do with being able to find your network and the people who are on your team you know being younger when I was in graduate school and not knowing how to advocate for myself or how to go out and build my own community whereas I think you know as you get older and you start to learn how to do those things you can create a supportive network anywhere that you work whether it's with the people you're you're interfacing with every day or people outside of work it's a lot if you you develop the skills to build your team and I know that you know Megan and David you're a very different company and I'd love to hear from you just about you know your perception of kind of culture and the data science culture there so Megan do you wanna go short so I can kind of speak to two sides of it right I can speak to it as sort of a community member you know part of the capital community and also in my role on the cattle team and I think I've found it incredibly welcoming it's you know Kaggle is a great place to get feedback under work and it's kind of interesting because my work that I put out there on cattle is literally my work you know it's my job yeah I found the community to be incredibly warm and welcoming and I've definitely seen it play out you know in you know some of the stories we've heard from competitors how they sort of started complete as complete novices and what they've learned to things like mentorship and then yeah within kaggle again I think I've I've yeah I found it a very welcoming environment um I think the thing that has made it most rewarding for me is having a culture where you can get feedback on your work and I think that's really valuable to somebody who is maybe new to a role that they can sort of feel comfortable sharing sharing their work that you know may be completely new to them they're not sure how to tackle it and then get honest feedback from co-workers is awesome David yeah sure so I think that's that's a great question and interesting for me too when I was making the switch over to data science one thing that my coach who had been mentoring me over the years said that you know you got a lot of experience at this company but be humble when you join it because you're gonna have to relearn a lot of things and I think that attitude has really served me well I think one of my biggest worries and fears was joining the data science group that everyone would be you know the data science genius where they could code in 17 different languages and they could do statistics in their sleep but what I found is it's really it's a broad range of experiences some people are great coders some people are creative statistics some people just have really good business knowledge and I think that by understanding your unique fit to the group and being humble in areas that you don't know and play in that collaboration I think that's worked really well at GE and and we are a team we're all jointly responsible for our goals and I think as a team to that GE it's such a new field data science how we're gonna do things we're also responsible is for is data science really going to stick or is it going to become a fad 20 years later like Six Sigma didn't died out so anyway it's very collaborative great at GE and I couldn't be happier awesome and so I want to dive into kind of this moment of making this switch right so you have a resume full of you know seemingly maybe unrelated experience or you know traditional data science experience so you know when you were interviewing for or looking for your first kind of data science role you know how did you effectively translate your skills into something that would stand out to school recruiting for data science yeah so I thought a lot about this as I was on my journey and I think that there's there's a couple of different areas that to focus on I think that if you're if you're going in today to science I think the first thing you have to look at and my entering this field that am I entering an area where I have a functional expertise for me that's finance so I think data science someone would want to pay me to do data science because I'm an expert at finance and that's what I've specialized in my career in the other area where you could go is you have a business experience for me that's GE aviation I've spent you know you know 12 10 years there so that's another area where people would pay you for that business acumen the hardest is if you want to do something where you don't have the functional experience and you don't have the business experience for me that's the Cancer Research it's why today I can't go get a job in cancer research because I don't have a functional experience or a business experience and that's why I build that up so I would recommend targeting companies where you have a functional experience or the industry that you work in and one thing I've done in the past even though it wasn't truly a data science role but in my last role with GE digital I was a project manager and I took the role because I got to work very closely with data scientist and I negotiated at the time that I took the role I told my manager I will take this role if you give me twenty percent of my responsibilities as data science work so I can grow my network and I can really start learning what it's about a GE so those are some recommendations that I've worked well for me and that I've noticed as I've been in this career field - kind of I don't know how to tell someone else to replicate it I think a lot of it was just dumb luck I was kind of I was done teaching I was reflecting a lot and at the time I was playing a lot of World of Warcraft like a lot of video game time and I was like I'm gonna go code video games that'll be totally cool like I'm just forget like save the world I'm gonna do something super fun and that I'm just super into and so got on Twitter and this company was looking for people to be Community Managers and I was like well you know I don't really like vlogging or but I can I can community manage and see where it goes and so the company was PvP live I was doing a couple of blog articles a week and we were having these meetings and I don't even remember how it happened but something came up where they found out that I knew about data and then it led to conversations like would you be interested in kind of a data position and then there was some back-and-forth and I realized like nobody at the company knew anything about data like I definitely came in with that expertise and so I was in this position where they were like here's a data set let's see what you can do actually no it wasn't even that they were like we want League of Legends data what can you do with that so I had to like go find the API and get an API key and doing all these things that I didn't know how to do but I knew how to Google so I turned around and I kind of submit this like finished piece of like here's my analysis of League of Legends and they were like great like let's hire you what do you want your job title to be so it was it was just like this weird situation with the startup of just being like I like video games I'm gonna take some volunteer expiry and then kind of roll it into a data science position and Megan cool I definitely like Jessie what you say about it can feel like sort of dumb luck so I guess like my advice I might start with like what I don't recommend doing and I think if you want to do like career switch in the data science I wouldn't recommend just sort of like burying yourself in your basement and you know taking a MOOC and not you know just kind of like you know putting blinders on I don't recommend doing that I definitely recommend doing that instead of like an iterative way so you know take a single MOOC and then try to apply something that you learn get something out there that's public show that you can execute a project and and to you know compete like a go competition you know and then go back and see you know what kind of signals did you get from that did you get feedback from the community did you you know maybe try experimenting with applying for a data analyst job and seeing what happens so get some signals about what's working try to get you know feedback from going to like meetups and things like that networking and then go back go back into your basement read some books read blogs you know decide you know plan out the next step and what you're gonna do based on sort of what you learned so I think that's kind of worked well for me what I do even today is every three months or so I sort of make a list of okay where's what kind of things do I need to do to you know in thinking about my longer-term career trajectory what kind of things do I need to do to make that happen how do I need to break it down what wait what kind of things can I do to sort of set myself up for moments of success even though a lot of times it can feel random so I want to point out an interesting overlap in what you said Jess and just um you know I was actually a part of bringing Megan in Chicago and I brought her in as a content marketing intern I so she was writing blogs as you know wasn't probably what you wanted to do every day for the rest of her life either and you know super late I think all of you have kind of somehow managed to turn new opportunity new opportunities that your existing in your existing wall your existing company by showing off your skills and you know they're hugely valuable to companies so that can be kind of one good way to parlay something into a data science room so you know what has felt like the biggest risk that you've taken in your career journey to become iDate a scientist and what gave you the confidence that you needed to take that risk because you know it can feel like a really big plunge a lot of time yeah you know change roles construction Megan do you want to start yeah I can deploy start and it's actually yeah quite easy quite salient for me it was definitely the decisions academia so you know starting a PhD in linguistics was I felt like a big like I found this trajectory I definitely felt like I was I was you know this is this is what's happening and I felt you know the full confidence that I was gonna see it through to the end and even when I was thinking of leaving it was a tough decision because I really did love what I was doing it was exciting to be you know paid to do research on acoustic phonetics and laboratory phonology questions about know how vowels are nasalized was what I was doing so yeah I really loved that so that's kind of why it felt like you know why give up a good thing know this is working I love the people that I was working with so I think that's that was I felt like the biggest risk was deciding to leave I ended up moving to from from Los Angeles to Pittsburgh where my my now husband was living we had in grad school at NC State where we both in the English department so I moved to Pittsburgh Pennsylvania without a job we lived in his parents bedroom and all I had was this confidence of okay I got the single Google interview I got like one bite on my resume but I definitely felt like the riskiest moment and all I could do is sort of like like I mentioned before try to keep putting things out there to get signal that I was doing things right or doing things wrong and then hoping that it would pay off and kind of like in the meantime continuing to kind of build my resume so I was doing online courses trying to I created like a personal portfolio like I'm a microsite web site that you know on a mentioned and you know what generate for my now role at kaggle so yeah doing things like that um I think helped me feel like it was less risky it was very scary David yeah sure so I think for me it's you know if you asked my wife she would tell you that her perspective is that I just woke up one day and decided to be a data scientist you know I left a very well known career that my entire career path had been in finance at GE and I knew the trajectory I knew what to expect and I knew the the technical stuff well where I didn't I didn't really need to learn in the evenings I could just kind of shut it down and relax and my data science opportunity came when I was up for a job change within finance and also at the same time we had just had our second child a baby boy so that was challenging to juggle a newborn at home as well as a new career field but thankfully we made it through and I used some of the lack of sleep and late night feedings to further study the data science piece but it's it is overwhelming and I'm fortunate enough to have been able to make that switch within the same company but you know to megan's when i think you just got to keep putting yourself out there and know that there's going to be a lot of times when you're making a switch into such a new field that people may not understand it you'll get a lot of notice but just you know keep developing yourself and believing yourself and eventually it will work out yeah for me I don't know um I was in a position where I needed a job I was kind of running out of runway it was it was not a pretty sight and I was working with their startup and they were like oh you can work remote for a year we'll see how it goes we'll reevaluate everything and about a month into that they called and they were like you can move to Dallas so you can find a new job it's you know so it was like I need a job I have this job but if it doesn't that in a little context I've always lived up north I've always lived places with four seasons I would consider Montana home I was living on a small farm outside of Bozeman it was fantastic was everything I ever wanted and I just I I needed to decide like am I gonna give up my one data science lead and try and make it work in Montana or am I gonna move to Dallas where if this falls through then then something else will will come up like I'll be in a good place to look for another job so so it was more necessity it wasn't so much risk but I still was very nervous about moving to Texas yeah and I think that's helpful for you know people that are nervous to make this switch and just that there is gonna be kind of a leap of faith probably right um and that that's just gonna be a part of your journey and to hold up the confidence and the skill set to do that like me and David we're talking about um so you know going back now that you've landed your first date of science job so I know some of you like David you're in your first date of science job um is it all you know if you thought it was gonna be and you know what ways have you been surprised I don't either were disappointed but like you know you probably build up a lot of expectations right you're very excited about making this career switch and then you know what is the reality but like yeah good David you in a start yeah sure I think for me the the biggest thought cuz I had immersed myself and like Kaggle and all the data science news was that everyone was on the data science bandwagon and it was taking over the world there was no moving back and then you you go into a company like GE Aviation that's very profitable that their long cycle business and it's actually kind of hard to get people to buy into your vision and that's why I really had to rely on the communication skills that I developed in finance of taking difficult technical concepts translating them to actionable tasks that people can do to drive value so for me it was you know having still to establish that brand within the company was something that I thought was really interesting and I had already thought that you know the entire company in the world was on that bandwagon and I think the other thing I wasn't fully prepared or expected was that and data scientists data science at GE anyway you really have to justify savings it's not a job feel that the company or a company will necessarily pay you to do if you're not seeing results I mean I can do data science on all sorts of fascinating things outside of work personal finance and things like that and actually do that but you really have to show results in that you're enabling savings and like I mentioned earlier so that this you kind of complete the legacy and prove that this function adds value and will be in the long term strategy for the company and Janica yeah so my first date of science job I really had this impression that I was going to show up and I was gonna sit in front of my computer and I was gonna code and do data science all day long it was gonna be amazing and and the reality was when you're at a start-up and we were still in single digits in terms of how big our team was you're not you don't have time to just do data science so I ended up learning a lot about UI UX and graphic design and project management and it was kind of this really cool experience to get a very very inside look at to how a business runs how does this startup run what needs to be done so on any given day I was doing data science I've pitched to VC firms which is kind of wild and you're just like what didn't you know me like you want me to do this and so it's fun this really great opportunity and I you know looking back on it I think it really works out well I think gave me a lot of skills that helped me continue to not only get data science jobs but to get data science jobs that also requires levels of leadership and management and and kind of people skills because it was very much being at a start-up and doing data science but also having this I this guy you know kind of can-do attitude like we need someone to make sure you know to sit on set and run minds with someone and like yeah I can do that that'll be great let's try it out and so kind of collaborating with people across all different different areas of the organization and it was there was a lot of fun and Megan sure um so I guess my first sort of data science II job I was actually a data analyst and a market research firm this is my first job after leaving grad school when I moved to Pittsburgh and I honestly felt very naive like I didn't know what to expect really at all and a lot was just sort of like ramping up to all these things that I was really unfamiliar with and then trying to figure out ways to apply what I knew about statistics and research to the projects that I was working on so yeah it's hard to say whether or not it was all that I thought it was going to be but it was definitely an interesting experience because I had I was confronted with a lot of ambiguity in my role I was the first hire of this sort of this sort of role for this company so I had to sort of like take a lot of agency and like how I was going to solve problems and how I was going to communicate why I made the choices I did and I feel that I learned a lot from doing that in that role and then now in my role as a data scientist Kaggle that's definitely a lot different and I think the biggest thing that I've learned in this role was the importance of communicating to different audiences this has definitely been something that I'm really glad to have had a lot of mentorship from people like Anna in particular it's just credit sort of thinking about how do I communicate this technical concept to people eternally how do I communicate it to different levels of expertise in the community what do I need to change about my message the delivery and so I think that was that was the biggest thing that I've learned that I didn't expect to learn I guess as a data scientist so yeah so we talked about this a little bit er I kind of mentioned it but I'd like to dig in a bit more you know all of you that sounds like have really turned found data science opportunities within existing rules which can be you know a really great way of making a career switch obviously um and so like what are some practical tips that you have for building a good reputation and network um kind of I somebody with data science capability is within your own company Jeff do you wanna start yeah I think a lot of it is getting to know people I have a really fantastic mentor and her kind of go-to is to take people for lunch get get out of the office and get with them eat lunch and get to know them as a person they're you know we talk about like work-life balance and like work life is separate from personal life but I think that when you get to know someone for who they are because you can't just leave who you are at home and come to work so so it helps break down barriers right like when you take them out for lunch you're doing something enjoyable you're out of the office and you're building that relationship and I really think that ultimately what you need to do to build a data science team especially if you're a team of one looking to expand or trying to justify the need for data science is you need buy-in from across the organization and and to do that you really do need to build relationships and and kind of influence other people no one's going to necessarily hire you to be the chief data scientist so you have to do a lot of peer management you have to manage up you have to manage the people above you you have to manage the people around you and I think I think taking about to lunch is a great way to start doing that again Hawaii yeah sure so I think sort of to kind of like piggyback on what Jessi was saying I think really understanding what is the landscape in the current you know your current job function so what are other people working on adjacent to you getting to know getting to know them their problems that they're solving and the things that they're generally thinking about and you know of course one way to do that is to set up one-on-ones of people to just kind of you know ask questions and I think once you've sort of like understood the landscape I guess the the where you're at among your co-workers in their projects you can identify them opportunities where you can say hey if I applied my skills here I could really make an impact and I think you know when you've developed relationships with your co-workers even if they're not people that you work very closely with you know they'll trust you and sort of you know you can find these opportunities to do I guess like 20% projects basically and I think that's a really great you know great way to have influence build a reputation of course you want to do whatever you can to sort of be successful in executing on the projects and the way that you used to like help their co-workers and again I would point to really understanding the products that they're facing is key to doing that sir and David yeah so I think just to echo on those comments before because I've made some great points to me you cannot underestimate the value that relationships play at work and I think that that's something that I really learned in finance where I need to understand the problems that the the businesses face in my counterparts and different functions in order to help them unlock value in have the organization reach its full potential so some of the things that I've seen that have worked well and data science it's similar to you know a cago approach that you would use and we use it finance to develop a quick proposal of projects that you're working on then get the buy-in through those relationships that you have and if you don't get the buy and then you scrap that project and you move on but I think you have a process that you use with your network that you can quickly identify projects that you're working on and that they have value you'll establish a reputation for success people want to rally behind you and work with you yeah and just you mentioned your mentor and make sure you a question come in just about you know if you if all of you have mentors and if you do how did you find them and kind of how that relationship helped you on your kind of career switch in journey Megan do you want to start I'm I haven't kept track of who's starting okay yes I'm definitely a huge advocate for finding mentors and I guess just I want to clarify I guess what I mean by mentor this can sort of follow along the spectrum of having somebody who's a formal that have dedicated mentor that you have like regular check-ins with and you know they know that you're their mentor and things like that and it can also just be like you know considering your co-workers mentors in like learning from the experiences they have and seeing their successes and understanding how they made their decisions that's like another way to sort of like have passive mentorship and I guess I yeah so I have a lot of people that I consider mentors ana is of course one of them another mentor that I have is somebody who is he's a Googler and he's also going through like a career transition as well to from like a more developer advocate role that I'm currently in he was he was formerly a product manager somehow he's a developer advocate I'm a developer advocate who's sort of interested in product management so I just reached out to somebody who's in my office and found him and now we can we get coffee every other week and he's just been a really great sounding board and it kind of goes both ways because you know I'm sort of trying to break into how do I have influence on a product through like community advocacy and speaking and things like that so that's been a really great relationship and as far as advice for finding mentors honestly this is kind of embarrassing but I just googled it there's actually a lot of like blogs about kind of mentors but I asked somebody to be a mentor and things like that so yeah and then oh the one other piece of advice I would want to give is be really open to feedback feedback is like absolute gold in your career and I think taking feedback well really reflecting on it and thinking like very very seriously about how you want to transform that feedback into changing with how you approach here your job is is incredible so yeah I really have you know I definitely agree with Megan on this idea that some of your colleagues are mentors and you have interpersonal relationships that can be mentorship and in my situation it was at my last position I remember sitting down with my my boss on my first you know one-on-one and she was like what can I help you learn like what is the one thing you want to get out of this and you know I knew I was good on the data and so I said to her I need to learn how to navigate office politics I need explicit advice on how to how to do this and she's phenomenal at it I mean she is like I I can't get over how well she is at office politics and so I was like will you please teach me everything you know and so it just kind of we met we started meeting weekly and even when I transition out of the position I still meet up with her once or twice a month for lunch and we just kind of talk it's more collegial relationship at this point which is kind of nice but I'm still like hey this is going on in my life and how would you handle this or like this person said this and they want to do this this way but I think you know I have a different approach and you know getting that feedback laic Megan says the idea of getting feedback on even how to approach projects and pitching projects and not even the data science side of things has been so absolutely invaluable to me and des then ya know I think this is a great question I don't you know a GE a lot of my mentors have just been informal but I do remember early on in my career when I was meeting with a CFO for a specific division and I was a couple of years into my career and he worked at the company 20 to 25 years and I remember him looking at me and saying he's like David you know the only thing that differentiates me sitting behind this big desk and you sitting on the other side and I said no and he replied that it's experience so I think that you want to reach out to your mentor to have them share their experience with you so that you can make good decisions on your journey throughout your career they say that history definitely repeats itself so that's to me what I look to a mentor for to help guide me when I get to Forks in the road to avoid making a wrong choice that my mentors perhaps could have had experience in that area Thanks and so you know a couple of you mentioned you know feedback and how important it is and we had a question come in that said how do you demonstrate that you have humility to learn and also receive feedback in kind of your application or interview and then I would also kind of add on you know how did you learn to take feedback David yeah no I think that early on in my career certainly getting feedback was hard and I think that one of my toughest managers she would just always you know yell and make you feel like you you didn't do a good job and I and I think that I struggled with that form of feedback but eventually what that experience taught me was that I had to separate my personal feelings from my professional feelings at work and once I realized that the feedback I was getting at work was how I performed and how I could be a better employee and not necessarily who David Avera was I was able to accept feedback much much better and grow from it I think during an interview I think coming in with just the the essence that you are humble and that you're you're listening you're attentive and that you ask good questions I think kind of shows that you have that ability to accept feedback and you can get that across with with your body language yeah yeah I think similar to what David has said I think that showing and demonstrating that you can accept feedback involves separating responses or reacting and responding right so when someone gives you feedback that might really sting you can you have a choice you can react and you can get flustered and you can get upset and you can do whatever or you can respond and sometimes that's taking a deep breath and asking questions I always ask questions so that I make sure I understand what is this feedback that I'm being given and if it's feedback about work and it's from you know someone who who is integral to my project success a lot of what I'll do is I'll go I'll try to implement the feedback and then check in a week later but even when I'm getting the feedback I might even say something along the lines of what does this look like to you so you know an example actually from teaching was I remember I was told that I needed to be assessing my students more often and I definitely react it I was like I don't have time to give a test every five minutes and she was like okay like calm down and she's like try again and so I she gave me the opportunity to respond and I was like what does it look like when I assess my students more often and she had five options and she's like this is what it looks like it takes less than 30 seconds each which - are you gonna try and I want you to do them for two weeks and then come back so that to me was just a really pivotal pivotal moment to be able to say okay you're in a conversation with people and someone's giving you feedback it's because they care they care on some level about your success so make sure you understand what they're giving feedback on and then make sure you understand what it looks like when you implement that feedback and then don't be afraid to say hey I've been trying XYZ and either a it's not working or B it's working great and asking for more feedback I think that's one of the best things that you can do to show that you're open to learning is to say I'm trying this thing and it's I need more help and it's it's not a weakness to ask for help or to ask for guidance on how to do something Megan yeah this is this is a really great question and it's definitely a tough nuanced question to answer I think definitely will echo what David and Jesse have both said I think in my experience like I've been really fortunate to have a lot of people that are just great at giving feedback that's been really valuable to me and you know what has made it valuables that I understand that you know the purpose of giving feedback is ultimately to further some shared goal that we have instead of like centering your response to that I think is a way to handle feedback and then you know way to so they get to that point like Jesse said just to ask questions and really make sure that you understand you know where is this feedback coming from it's yeah completely agree with that and then as far as sort of demonstrating it in an interview kind of context and demonstrating that you have humility I think if you have opportunities to sort of like tell a story about a time that maybe you made a mistake or something that didn't go as planned and sort of like how you learn from learn from that I think definitely can show humility in that you're you're you are receptive and open to like feedback signals and you use those to improve your work and David I want to make sure to ask you this question I came in a while ago but honey wrote I have two children with cystic fibrosis and want to find avenues through data science to improve treatments and help find a cure what platforms and organizations kind of look to for opportunities yeah I'll be on the the slack tonight there was actually one of my friends and GE she has actually two daughters with CF so I definitely she is active in the community and I could get definitely connect you to reach out I think part of the journey that you're on is knowing that you're not alone so I'd be happy to make that connection for you thank you and I know we were kind of getting close to a time so it's kind of a final question I'd love to know just looking back at your careers is there anything you would have done differently any kind of decision that you regret I know you know it might be easy to feel like I wish I had started in data science I wish I had two answers right at the beginning so you know what is your perspective um John Sirica sure I think for me I got really good at figuring out things kind of as I needed them and so if I could do it all over again I think I would have spent a lot more time being a little more disciplined about my learning rather than just kind of grabbing things as I needed and I wish that I hadn't been afraid of github for so long I really really put that off for a long time it was like no I can't put anything out unless it's perfect and then I just kind of was like who cares like let's let's put out garbage code because that's the only way I'm gonna get better is if someone comes along I was like hey that's garbage code let's make it better so yeah I really wish that I had I had started github a long time ago instead of you know not so long ago it's great advice Megan sure yeah I thought about this question and honestly I wouldn't want to go back and change anything because I'm very happy where I am right now I feel like I've been very fortunate and I feel like I'm in a great trajectory so I really honestly wouldn't change anything but if I knew that going back and changing something wouldn't sort of you know alter significantly where I am today I think I would have probably studied computer science any other high school or college in some capacity at all in my infographic I pointed out that my dad pushed me to study either computer science or engineering and you know you really kind of fought at that and said no I'm gonna become a musician and you know I think from an early age I really had like a strong affinity for computers I was very curious about computers and coding and things like that and I feel if I could go back I would have sort of like fostered and developed that from an earlier age but David yeah no I think this is a good question I think for me it's you know probably the first 10 years in my career I would always take a job based on you know hiring me interesting you need to check this box check that box do this and that if you want to reach this place and I think that you know that can quickly lead to burning out and losing sight of what you value and what you enjoy doing so I think it from my perspective it's it's just knowing that you own your career and no one's gonna manage it or look out for you but yourself and I would caution anyone not to take a job just because someone has an opening in their organization you know take a job because you're passionate about it that you you want to do it and understand the the path for a career but you're in charge and just don't don't give up believe in yourself and if you're passionate about native science you know go for it just be patient and measured at the same time because it's a new field and it's as we've talked with all our lining past sometimes even when you're pushing so hard an unexpected opportunity may and most likely will come up and I think that there's opportunities at at least some of the companies that we have they representing is that you David are you yeah we're actually I push really hard to try to get the opportunities posted on kaggle but I couldn't jump through the HR hoops in time but GE will aviation will have several data scientist rules posted on Kaggle here in the upcoming week so I'm excited to talk more and I'll be on the the slack networking virtual networking event later tonight okay they're there any opportunities at teaching history no fault on profit so opportunities are fairly limited or starting conversations in early April to to really look to bring in someone to compliment our data science by doing database architecture and development we haven't fully scoped out that role so I don't know that the ins and outs of it but I can tell you somebody who can who can build beautiful databases and then keep them running is definitely someone we're looking for cool and may God give you a plug sure um so I think maybe this is highlighted another session but we are hiring competitions data scientists I think that role is on the jobs for our jobs board but if anybody has any questions about working for cattle you know what it's like happy to answer any questions like that in the in the slack well thank you so much so the super interesting and I think inspiring conversation in a lot of ways

Channel: Kaggle

Views: 10,344

Rating: 5 out of 5

Keywords: Kaggle, Kaggel, coffee chat, live-coding, live, learn, api, cli, python, data, data science, interview, questions, transfer learning, coding, networks, programming, technology, tech, machine learning, AI, artificial intelligence, coders, programmers, help, tutorial, projects, 101, rstats, stats, statistics, what is kaggle, how to, github, developer, kernels, datasets, data visualization, deep learning, sql, challenge, competition, whitehat, code, lesson, CS

Id: iP0Fxg4oqUQ

Length: 57min 51sec (3471 seconds)

Published: Thu Mar 22 2018