Kirill: This is episode number 379 with LinkedIn Learning Instructor, Christopher Bishop.
Kirill: Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, Data Science Coach and Lifestyle Entrepreneur. Each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today. Now, let’s make the complex simple.
Kirill: Welcome back to the SuperDataScience podcast everybody, super pumped to have you back here on the show. Today we’ve got a very special guest, Christopher Bishop who is joining us to talk about careers, how to identify the career that you want. So this podcast will be very useful to you if you’re just starting out into the space of data science, or you’re transitioning into the space of data science and you might be overwhelmed, or you’re still undecided in which direction you would like to go. Christopher came up with a very interesting framework, which is based on his personal career, which started off in a completely unrelated field.
Kirill: Christopher has a bachelor of arts in German literature. Then he ventured into music and then he ventured into IBM into the corporate world. Finally, he became a data science career advisor. Based on his personal career, he’s come up with a framework that will help you identify what you’re passionate about, what your voice should be about. Then he talks about step two, which is the antenna where you should get the information to drive your passion, to feed your passion. And then he talks about the mesh and how to create that network around you to be surrounded by people who are also passionate about the same thing and open up yourself to new opportunities. So it’s the framework of voice, antenna, mesh.
Kirill: Christopher has a course on LinkedIn Learning about this called Future Proofing Your Data Science Career. In today’s podcast, he is sharing the ultimate tips and hacks from there, so you can apply it to your personal journey. Very exciting podcast coming up. I actually tested it out on myself during this conversation, so you’ll hear that as well. I can’t wait for you to check it out. Without further ado, I bring to LinkedIn Learning instructor, Christopher Bishop.
Kirill: Welcome back to the SuperDataScience podcasts everybody, super excited to have you back here on the show. Today’s special guest, Christopher Bishop is calling in from Connecticut. Chris, how are you doing today?
Christopher: Hi, there. Welcome from the east coast of the US. Delighted to be-
Kirill: Amazing to have you on board. How’s things on the East Coast? Are the lockdown slowly easing off, or is it still quite restrictive?
Christopher: It’s pretty restricted. I mean, they’re saying it’s going to start to ease up more next week. And I can finally get a haircut after six months of looking like a pretty raggedy character here, but it’s good. The weather has been great. It’s summer up here in this hemisphere, though we’ve had some lovely summer days here in the Woodburns.
Kirill: Fantastic, that’s fantastic. You said you’re only an hour away from New York, or not far away from New York.
Christopher: Yeah. We live in, it could just be described as the commuter corridor. So a lot of people get on, used to, not so much anymore, but you used to get on trains and go into New York from here, from Connecticut every day. I did it myself every day for eight years in and out. It’s a long trip. I was doing it before really there were laptops and cell phones, [inaudible 00:04:12] myself. But you can do a lot of work now on the train. So, it’s not bad. That’s where the work is, so that’s where you got to go.
Kirill: Okay, okay. Gotcha. Well, what’s your favorite thing to do on that commute?
Christopher: Well, typically read the paper going in. Usually I [inaudible 00:04:33] reading the actual paper, now I do it on my phone. I’m going to read a book or listen to a book on Audible or use Blinkist. I’m a big fan of that application to little snackable bites of books that they put together, that’s pretty cool.
Kirill: Yeah, yeah. I’ve read a few on Blinkist. It’s quite useful.
Christopher: Yeah, very helpful. Books that I would never read that I just want to get a sense of what they’re about, you can get through it in 20 minutes, you get the basic idea. There’ve been instances where I’ve actually gone back and read the actual book a few times. But mostly it’s like, oh okay, that’s what that’s about. Okay, next.
Kirill: Fantastic, fantastic. Okay. Well, tell us a bit about yourself. You are quite active in the space of data science, specifically even helping people and educating people. For somebody who doesn’t know you, how would you describe the things that you do?
Christopher: I describe myself as a non-linear, multimodal careerist. By that, I mean, I’ve had eight careers so far since I graduated from college with a degree in German literature, really handy.
Kirill: Wow.
Christopher: But I was also minoring in music. Right after school, I got a gig touring with a band that was opening for the Eagles and ZZ Top and Fleetwood Mac and Frank Zappa. I moved to New York, became a session musician, toured with Robert Palmer, did two tours and a live album at the Dominion Theater playing bass for him. Ended up in the jingle biz in New York, writing music for television commercials. I taught myself to be a web producer in the early 1900s. Much to my surprise was hired by IBM into corporate internet programs and worked there for 15 years.
Kirill: Wow.
Christopher: So I was getting in the right place at the right time. I would say that to data scientists as well, be aware of those kinds of transitions. So I was in the jingle biz and creating music on a Synclavier, which was the state of the art digital musical instrument at the time, music is data. I always say music became data about 1985 in New York, when guys and girls were samplers and sequencers came in and they could replicate basically what a whole room full of musicians were doing with a rack full of equipment.
Christopher: Again, I think where those kinds of transitions around technology in business are going on today in the context of data science. So, just a heads up. After IBM, I did a TEDx talk. And then transitioned into freelance consulting about future of work writ large, and more specifically about how technology, and in this case, data science, where driving transitions for business models and what they represent in terms of career and job opportunities.
Kirill: What years are we talking about?
Christopher: I left IBM about seven years ago, 2013. Not to disparage IBM, but I did a TEDx talk and then they gave me a package. So, that’s-
Kirill: Gotcha.
Christopher: … what they value. But anyway, I worked at a company called Future Workplace as a boutique HR consultant for a couple of years. And then my real passion is talking, not to HR people with all due respect, but talking to young learners and workers. So the segue is speaking at various conferences. I connected with LinkedIn Learning and they gave me an opportunity to create a course called Future Proofing Your data Science Career. That’s available now on LinkedIn Learning.
Kirill: It’s amazing, I have it up here in front of me. I’ve watched a few course, and I’ve mentioned this before in the email, I’ve watched, I think two or three videos and I got so hooked. It’s on my to-do-list to watch the whole thing. I’m really excited. What I like about it, it’s only one hour, four minutes long. But even in those couple of videos that were available as free previews, I understood that you have a very interesting way of positioning, as you call it the Future Career Toolkit, what is important and how to think about your career to future-proof it. That’s exactly what I’d love to dig into this podcast because I think that’ll give a lot of value to our listeners. Many of whom are looking to break into the space of data science or transition into the space of data science from a different career. Maybe let’s kick things off.
Kirill: You mentioned three main concepts in your course, the voice, the antenna and the mesh. First of all, tell us a bit of background. What kind of thinking, what kind of experiences in your life gave you the material to create this framework, to create this course? Clearly it didn’t come out of nowhere and not from somewhere else, because I’ve never seen this before. Is it personal experiences, is it people you’ve spoken with, people you’ve coached, mentored? I’m just real curious, where do you get the raw material for the course?
Christopher: Yeah. Well, the trigger actually, the catalyst was that I was invited to give a keynote speech to kick off a series of senior week activities at my Alma mater, which is Bennington College. It’s a small liberal arts school in Vermont, another state in the US. As I began to put this speech together, I look back and say, “Well, I guess I’ve had a bunch of different careers,” and begIn to think more formally about how I navigated through them and thinking, is there some way I can codify how I made these various transitions? With [inaudible 00:10:18], it’s been a pretty interesting journey so far.
Christopher: I don’t think you meet a lot of guys that played with Robert Palmer and then worked at IBM Corporate headquarters. There maybe a few, but … Over years really, I analyzed my transitions and what I did, what was consistent about going from one of these careers to the next, I put together as Future Career Toolkit thinking about, and what the tools might be because the idea was, how could my experience be codified to benefit the next generation. Now, I’m at that point where I’ve done a lot of different interesting things.
Christopher: I think, again, today’s learners are going to follow a model similar to the way I’ve lived, the way I’ve worked. I mean, US Bureau of Labor Statistics says, today’s learners are going to have eight to 10 jobs by the time they’re 38. Other research says, 85% of the jobs today’s learners are going to do in the next decade or so, haven’t been invented yet. They’re going to use technology that doesn’t exist. That makes things like cell phones look antiquated. It’s going to be like, “Grandpa, you mean you had to carry something around in your pocket to talk to grandma? That’s pretty lame. Wow, what [inaudible 00:11:32] technology that is?”
Kirill: Wow. It’s moving fast, it’s really moving fast.
Christopher: Yeah, it’s moving fast. So, that’s the Genesis of the tools. I sat down and just really over years, thinking about how to put it into some simple codifiable set of techniques that people could use and in this case, data scientist is for sure.
Kirill: Understood. Why data science though? Sounds like you’ve had many different careers in different areas from music to data science to corporate. Why did you choose to specifically focus on helping data scientists succeed with their careers?
Christopher: It was the connection with the Open Data Science Conference. They had reached for me about speaking, maybe they’d seen my TEDx talk, but they wanted to see if I could take my perspective in this toolkit approach and apply it to data science. So, the first thing they had me do is, I went to London in September 2018 and spoke about AI actually, more specifically at the Accelerate AI Europe. I put together a talk called Your Brain’s too Small to Manage Your Business. It was about the commoditization of AI. I picked out four categories of partners or vendors or startups that could help companies understand how to apply AI. And then they asked if I would do a talk at their event in New York about how to succeed at data science jobs that don’t exist. So I do these workshops mostly at universities. I’ve done them at Columbia, at NYU Stern, Baruch, Duke, Texas, A&M, Queens College.
Kirill: Wow.
Christopher: A lot like B-schools, business schools talking about future careers and again, how to succeed at jobs that don’t exist yet based on these tools. So the ODSC people asked if I could, again, do a specific version focused on data science. So I put that together. And then people from LinkedIn saw it and asked if I could do a video version of it and make a course out of it. So I said, “Yeah, I’d love to do that.”
Kirill: Amazing. It sounds like your life is a chain of really random events, one after the other.
Christopher: It is. But again, with very studious networking behind it all, like how I tracked you down, in fact.
Kirill: Yeah, yeah. That was amazing. Are you subscribed to the Data Science Insider? Personally, I love the Data Science Insider. It is something that we created, so I’m biased, but I do get a lot of value out of it. Data Science Insider, if you don’t know, is a free, absolutely free newsletter, which we send out into your inbox every Friday. Very easy to subscribe to, go to www.superdatascience.com/dsi. What do we put together there? Well, our team goes through the most important updates over the past week or maybe several weeks and binds the news related to data science and artificial intelligence. You can get swamped with all the news, even if you filter it down to just AI and data science. That’s why our team does this work for you.
Kirill: Our team goes through all this news and finds the top five, simply five articles that you will find interesting for your personal and professional growth. They are then summarized, put into one email. And at a click of a button, you can access them, look through the summaries, you don’t even have to go and read the whole article, you can just read the summary and be up to speed with what’s going on in the world. If you’re interested in what exactly is happening in detail, then you can click the link and read the original article itself. I do that almost every week myself. I go through the articles and sometimes I find something interesting, I dig into it. So if you’d like to get the updates of the week in your inbox, subscribe to the Data Science Insider absolutely free @superdatascience.com/dsi. That’s www.superdatascience.com/dsi. Now, let’s get back to this amazing episode.
Kirill: Okay. Now that we’ve built up this anticipation and … even I’m keen to find out, tell us, please, what is this Future Career Toolkit that you talk about in your course?
Christopher: Okay. The Future Career Toolkit has three pieces. Again, I’m trying to keep it simple. So the pieces are voice, antenna and mesh. I work closely with a guy here in Connecticut, who’s an ideation guru. He does ideation sessions for big corporate customers who does sessions to help them create new products and services. He has a whole portfolio of techniques that he uses. So he and I collaborated to put together some of these activities, particularly with voice. So the voice is the first piece.
Christopher: Really what it is, it’s a process for finding your own value prop. It’s almost like product development, product definition, if you will. We use these triggers, we ask participants or learners to pick their favorite movie, TV show, book or even game. We did a session actually in a high school, and this one kid said, “I’m inspired by Fortnite.” I use that as my trigger. So we asked them to pick something that resonates with them, and then tease out what the characteristic is. For me, for example, my favorite movie recently was Blade Runner 2049. So, the reason is I’m interested in future technology. I like the future culture perspective, how the world might change, even his love interest, I mean, what about a robotic VR girlfriend? I mean, the implications around technology and how it influences culture are pretty broad in that movie.
Christopher: My favorite book was a book by an economist, Ruchir Sharma, The Rise and Fall of Nations. It’s about how economies move and change and grow, very often driven by technology. One of the technologies being data science. So, at a meta level is future technology and it’s got a global economics, are the triggers. So that’s what came out of my voice exercise. Again, every person is different obviously, but it’s a way for people to get to a sense of what they’re interested in, what they’re passionate about, what makes them want to get up in the morning, what they’re excited about. And then the next phase is to talk about how that translates to careers. First tool is the voice tool, right?
Kirill: Mm-hmm (affirmative). Okay, okay. That’s exciting. Well, let’s do it for me, if you don’t mind. Let’s just do it right now for me-
Christopher: Absolutely, let’s do it.
Kirill: All right. What’s the first question?
Christopher: Pick a favorite book, TV show, movie or game. It can be from your childhood, it can be from last week, whatever comes to mind. Again, first thought, best thought, don’t over-
Kirill: Oh okay.
Christopher: … what pops into your head.
Kirill: All right, okay. In terms of book, I really like this book I’m reading now. I’ve mentioned on the podcast several times before, Deep Work, about focus, about isolating yourself from any kind of distractions and being able to be very productive and get to the most every day. I feel fulfilled when I’m able to do it. In terms of movies, the first thing that pops to my mind, my girlfriend and I, we were watching a movie just yesterday and I really, really liked it. It’s called Inside Out. It’s by Disney. It’s about … you know that one, right?
Christopher: I love that movie, that’s a great movie, man.
Kirill: Yeah, yeah. I guess, it really describes well psychology of, even for absolutely accessible to anybody of joy, sadness, anger, disgust and fear. I like understanding people’s psychology. So, that would be my answer off the top of my head.
Christopher: Right. I would say based on the movie, that on the movie, I love that movie. I [inaudible 00:19:52] amazing movie. If people listening to podcast haven’t seen it, I would encourage you to check it out. Very much deeper than it seems. I mean, it’s a cartoon, but the stuff they’re talking about. You have an interest in psychology in the way the mind works. I think the broader application is how to maybe control or manage the mind based on the book you’re describing. So maybe implications are, this is what we use to drive the antenna piece, is where our conversations going on around psychology and bigger picture thinking.
Christopher: Certainly I would say based on what you do tied to data science, so maybe it’s neuromorphic computing or maybe it’s areas where psychology and data are connecting, they’re now analyzing brainwaves. They’re using brainwaves to manipulate physical objects, I mean, prosthesis and stuff like that. So that would be-
Kirill: Interesting, interesting.
Christopher: … that would be my take on what you’re describing.
Kirill: Gotcha. Or something like Neurolink where there’re brain implants to brain computer interface and like things.
Christopher: Yes, exactly. The connection between psychology and brain function and technology and data.
Kirill: Okay, fantastic. So that’s my voice. That’s one of the avenues that I could explore to become … because I see what you mean. If I start digging into that and sharing more about these topics, I will find it interesting myself and I will be able to keep going. I won’t find it as a chore, I will find excitement in it and that way I will be able to dig into insights that no other people would find tedious and, like oh, I have to do this again. For me, it would be just a breeze.
Christopher: Yeah, because these triggers have teased out or bubbled up what you think is interesting. The implication is what a future career path might be or an additional or adjacent career path here. It doesn’t have to mean you stop doing one thing and do something else, but again, these triggers tease up, oh yeah, that is interesting to me. Yeah.
Kirill: Okay, valuable. I see.
Christopher: Make sense?
Kirill: Yeah, makes sense, absolutely. All right. Let’s move on to the second step.
Christopher: Okay. The second piece is antenna. What you do in the antenna exercise is you try to track down, don’t try, you do track down, where conversations are going on around the topics you teased out of the voice exercise. The idea is you put together sources, the topic area and maybe it’s a more nuanced version of the major topics you tease out of the voice exercise. And then what kind of channel it is and what kind of source it is? And then the key is frequency. So how often are you going to check to see where new information around these topics is going on?
Christopher: For an example, there’s a TV show called Bloomberg Technology that I watch almost every day. It’s on 5:00, it’s produced in Silicon Valley. They talk about technology and business because they’re just north of the South Bay. They’re very focused on, not only what the Fang or the major companies are doing in technology, Google and Amazon and Microsoft and Twitter, Facebook, but they also look at startups. They talk to all kinds of reporters and their journalists to track just what Facebook is doing. To be honest, they treated a little bit the way Entertainment Tonight treats Hollywood stars.
Christopher: Now they’re gushing and sometimes way too granular, but they talk about trending in technology and business. So for me, that’s exciting, that’s interesting. I want to see where the money is going, where is venture capital getting invested, what companies are being acquired or creating new technologies that might transform business or culture. That’s one example, and that’s daily. The New York Times I read daily. There’s a show on the BBC called Click. I love the BBC, and that’s a weekly show. They talk, again, about leading edge technologies. They’re a little farther out than Bloomberg Technology TV show. They’re talking about things that are maybe still in university labs or even in the bowels of corporate R&D settings. But that’s again, interesting stuff that’s on the periphery that’s going to probably eventually, to some degree, it’s going to work its way into the mainstream.
Christopher: For you, I would say, look for where … like the Neuralink. Start with Neuralink. You put their website in your list. Again, if you’ll see in the course, I built a framework like a grid. The left column is the trigger. So in your case, it’s psychology and data or brain function and data and technology. The next column is the source, so that might be the Neuralink website. The next is that it qualify that it is a website, so you don’t get too skewed in one direction and you want to have a range of sources. And then the final column is frequency. Maybe you check it every day, every other day. You see who’s writing on the Neuralink website, is there a blogger or a professor or an academic or a thought leader? Do they have a separate blog? If they do, find where that is, and maybe check that, depending on how frequently they update it, maybe once a week or whatever, every two weeks. Is there a LinkedIn group or conversations are going on around brain-
Kirill: I was about to say, where’s the community, where are the other people like that who are also interested in these things?
Christopher: Yeah. So thought leaders, community, all kinds of sources, you know that … As I said in the course, the good news is there are lots and lots of different sources of information. The bad news is there’s lots and lots of sources of really good information. So, the challenge is winnowing and performing triage and rationalizing. I’d say pick three to five to get started. They’re going to change and morph over your multiple data science careers, but pick three to five, a manageable number and that are arranged. Aren’t just websites or the elite newspapers or LinkedIn group, make it a bunch of different sources. So you’re cutting a wide swath in terms of places to get good information.
Kirill: Yeah. Another tip I could give people is set up a Google notification. You can go onto Google and tell it to notify you every time there’s something related to Neuralink that comes out. It can notify you on a daily basis or on a weekly basis or monthly based. I don’t know how often you want it, it just comes to your inbox and it’s just a summary of all the articles. You’re going to click and read about them, that way you don’t have to go out searching for them as well.
Christopher: Yeah. That’s a great tip. I’m a big fan of Google Alerts. I’ve actually even set it up. So I have three main categories and it sends me a daily digest. So it aggregates them all. So, it puts them in one place, which Is … that’s a great idea.
Kirill: That’s right. I said it wrong, it’s Google notifications, it’s Google Alerts, right?
Christopher: Yeah. Google Alerts, yeah. That’s a great tool for sure, yeah.
Kirill: Okay, awesome. I found this, I’m participating or I’m reading with a frequency that’s acceptable to me. What’s the next step?
Christopher: The next step, the third piece of the Future Career Toolkit is what I call mesh. I think of it as a 3D data visualization exercise. Years ago, LinkedIn used to generate what they called an InMap. I don’t know if you remember that, but it was a color coded-
Kirill: Oh yeah. Yeah, yeah. Like all the connections. Yeah, that’s huge.
Christopher: It’s huge, yeah. All color coded. It was-
Kirill: Why did they stop? I wonder.
Christopher: I have to conclude that the server load didn’t translate to attributable revenue on some level. I mean, somebody said, “This is fun though, but it doesn’t make us any money. So I think we should shut it down.”
Kirill: Yeah. There is still a good software for that, I forget the name, but if I remember we’ll include in the show notes, that is able to do that, like visualize networks of people exactly in the same way. I’ll think of it later. But if somebody wants to do that, I think you can export your connections from LinkedIn and then visualize it like that.
Christopher: Oh, cool. So that’s what this exercise is. Again, I describe myself as an inveterate networker. Actually I wrote a piece on LinkedIn recently called How to Network in Your Pajamas. We’re all in these virtual settings now, but you can see and look at participants and who’s posing questions and look at the chat and check out the Q&A, and find people to connect with. I mean, when I moved to New York in 1976 to make a living as a bass player, I reached for Willy, who’s quite a famous bass player in New York, who’s a friend. “So what do I in the New York?” He said, “Schedule six jam sessions a day and panic to get to the next one.” You know what I’m saying?
Christopher: What does that mean? He said, “Eventually one will turn into a gig, a paying gig. And then do five jam sessions a day and do the gig. And then do two gigs and four jam sessions.” That’s a metaphor for, it’s not what you know, it’s who you know. You’ve to let people know who you are. So, that model still holds true in 2020 in the era of COVID-19. I mean, you’ve got to let people know who you are. So, the mesh piece is about building a robust complex network and doing it on an ongoing basis across a range of topic areas. What you do is you take the antenna content, you’ve done that exercise, you have the sources. So now you drill down into that data, write the data and see who the actual people or companies or organizations where these kinds of conversations in the case of what we discovered with you, Kirill, like Neuralink or brain machine interfaces, who are the people that are doing that work?
Christopher: I encourage people to do a LinkedIn search, for sure. You could do a Google search, find out where people are having this work actually done. So, I think of the Neuralink thing, I think of certainly Silicon Valley. I mean, that company that Ilan is funding. I mean, at the MIT Media Lab, there’s stuff going on. So find out who’s doing that work and reach for them on LinkedIn and get on their proverbial radar. Send them a connection request, tell them who you are and what you’re doing. If you can’t, have an in person meeting maybe next year when this all passes. But at the very least, have a Zoom meeting. Say you’d like to get 15 minutes on their calendar just to introduce yourself and learn more about what they’re doing.
Christopher: I think you find people are generally very responsive. The astute ones realize we run a global community nowadays. There are a lot of really smart interesting people out there, and you don’t want to miss an opportunity to connect with somebody. So, that’s what is in my admonition is me wagging my finger at the camera for those of you watching the video. Add five people a week to your LinkedIn profile, make it your job. I especially say this to gen-Z learners and early career millennials. If you sit down at 5:00 on Friday and you haven’t added five people to your network, get to it. You use criteria, again, from the voice and antenna exercise. And use those as search criteria and track down somebody in neuromorphic computing, in your case, Kirill, or brain machine interface, use that as a criteria.
Christopher: You can narrow it down, if you want to look into particular vertical, if you want to know who’s doing that and say automotive or travel and transportation, or doing it in retail or in energy or education. Find people and get connected to them because there is a way-
Kirill: Not just adding random people like hundreds by the hundreds. You want to read and understand. Read about their work and do a conscious connection on LinkedIn.
Christopher: Yeah, absolutely. It’s going to change for sure. I mean, to be fair, just the way my careers have changed. Whatever data science role you’re in now, odds are good, it’s you’re going to do something else because you can, not because you have to because there’s lots of opportunities. I mean, typical years of service now start 18 months to three years. That’s a general number at most big corporations, for sure. You may work at a big company, might work at a startup, you might start your own company, you might sell it, go to another company. So, all that I’m saying that your mesh is going to change in more.
Christopher: I mean, if you look at my chart around my mesh, there’s a big clump of yellow that I’m musician because I was a professional musician for 20 years. There’re blue codes, the IBM context, 15 years at IBM. I have all sorts of futurist cluster, if you will, because I did Workplace Futurist. I have a whole data science set of connections as well. So that’s-
Kirill: Okay, okay. Gotcha. Interesting. A couple of questions. This is my first question. Somebody listening to this might think, Chris, is very easy for you to connect. You’re very experienced. You’ve been in the industry. You have a brand, people would love to connect with you as well. But if I’m just starting out into data science, if I’m just beginning my journey or I’m a data scientist with just a few years of experience, I don’t feel confident that people I reach out to will be open to connecting. Yes, we live in a golden network, but they get so many messages, they’ll probably get lost in their inbox. What are some of your tips for reaching out and doing this effectively, connecting with people and showing them that, hey, this is going to be really worth their time or you’re not just another random person that’s connecting for no apparent reason, you’ve done your research? What tips do you have?
Christopher: Yeah. First of all, I’d like to sidebar plug for the conversation you and Kate had, encouraged people to go to LinkedIn Live and check out Kirill’s conversation with Kate about branding as a data scientist, very cool. I would also say, ultimately, the relationship has to be give and take, quid pro quo. You have to bring something to the conversation that’s of interest to them because, again, the risk of sounding crass, people in roles of responsibility at companies or startups, whatever, they’re trying to make themselves look good. They’re trying to do a good job for whomever they work for, even if it’s their company, in which case they work for the market, they work for the marketplace. So the key is understanding what your brand is, your voice and then representing it. So I encourage everybody, data scientists, to think about how you’re going to represent your work. So, do you point people from your LinkedIn profile to GitHub, to Bitbucket, to an actual application that’s out in the wheel. Maybe there’s an app on the Google Play Store or whatever?
Christopher: You need to be able to share your work, show what you’ve done. If you’re still just starting out, maybe even there’s a capstone project you did in school or something interesting you put together or hackathon you participated in, just to give yourself credibility so they can see, oh, this guy or girl did something interesting. They’re starting out, but they participated in a hackathon, or they wrote a piece of code that’s on GitHub, or they get a lot of good reviews or … If you do a talk somewhere, post it on SlideShare and point to it from LinkedIn. If you put together some charts, a presentation, even for a team meeting or even for a class if you’re still in school, create some deliverables, we used to call them work products at IBM, and put them out there where people can see them. Don’t be afraid to point people to them. It’s a little bit of boasting, it’s a little bit of acting, it’s a little bit of performing all those things, but be confident that you have something to offer and people will respond.
Kirill: That’s some great advice. I would also add to that something that worked for me. Something that someone else has done and it worked for me. I was very surprised at how effective it was. If you write a blog post, and say the top five influencers in the space of brain to machine interface, and you list the top five people that you truly believe are the influencers there and the ones that you want to connect with. So you just write a blog post, you publish it. You promote it a little bit somehow through your network or through Medium or somewhere else, and you get people to read it. And you tag those people, eventually they’ll notice that they’ve been tagged in a blog post. They’ve been named one of the top five influences by this person. Who’s this person? Who are they? Clearly they have interests.
Kirill: Like when I was tagged like that, I instantly went, because that blog post actually made … was very useful to people. A lot of people saw it and they were like, oh cool. Who should we take courses from or who are the people that are helping the space of data science? I was happy or proud to be mentioned there. So I was like, oh, who wrote this blog post? And then I reached out to that person, invited them to the podcast. It was a really cool unconventional way of getting somebody’s attention. I recommend that. I think that strategy would work for at least a couple of the people on the list. I think they would notice it.
Christopher: Yeah, absolutely. Similarly, on Twitter. I mean, if you have a Twitter handle, mention some … When I go to events, I attended Cogex last week, a virtual event in London, take a screen grab of somebody speaking and tweet it and put their handle in the tweet. And then they’re going to like it, or retweet it or whatever. Again, you’ve made some kind of connection, some kind of digital connection with them.
Kirill: Yeah, fantastic. There’s lots of creative ways. You just got to get creative and then you’ll find a way to make it happen.
Christopher: Be bold, be proactive. I mean, encourage today’s learners and workers to … put yourself out there. Again, define what your brand is. And then don’t be afraid to represent it because there are lots of ways to do it, as you’re saying, on LinkedIn, on Twitter. You do it on Instagram, lots of channels, lots of … and for free. LinkedIn is a great publishing platform too. I mean, I don’t publish nearly as often as I should, but you can put up a post and then tag people and get people’s attention. Certainly people in your network will view it and then they’ll point other people to it. It’s a great tool.
Kirill: Okay, all right. Speaking of the mesh, another thing you could do is once you’ve connected to one of these people in the space because you are interested in this space more and more, you can ask them, “Hey, can you connect me?” After a few chats, “Can you connect me with X and Y?” Who you know they’re connected because of LinkedIn. So, that’s another technique.
Christopher: Yeah.
Kirill: I wanted to ask you …
Christopher: I’d like say, I described that as the Twitter math. So when you follow somebody or you connect with somebody on LinkedIn, go to see who their connections are. Scroll through and pick out people that look interesting that they’re connected to and reach for them. Same with Twitter too, it’s a great way to see who they follow. That you can do without even following them. Just see who is on their list, that’s an open data base, a data set of people that they think are interesting or important, or contributing to the discipline, and follow them. You will find then on LinkedIn and reach for them. Again, lots of ways to do it, to work the tools to expand your mesh.
Kirill: Absolutely, absolutely. Other question for you, can you have multiple voices at the same time? You mentioned that your voice might change, over time, you might be interested in different things, but can you have multiple voices at the same time?
Christopher: Yes, I think you can, absolutely. The example I cite is a woman that I know who worked at IBM, she’s a millennial. She was actually there after my time, but I remember … I think this is somewhat of a fairly typical millennial worldview or approach. Her name is Samantha. She would say, “Well, yeah, during the day, I do social media at a global 50 tech company.” So she worked in corporate headquarters supporting the then CEO, Ginni Rometty. She said that, “At night, I’m a seamstress, I design and make clothes. And then on the weekends, I’m a DJ.” So, she has about three concurrent careers.
Christopher: Now for me, I still do gigs. I still play in three different bands. I just did a session on Sunday. The keyboard player lives in the next side over and he’s got a barn. We played last summer at this festival called Sailfest in New London, Connecticut right on the water. This year, it’s virtual. So they asked if we would put a video together. So we put together a little vignette. We called it a soft day’s night. Take off on the Beatles movie, A Hard Day’s Night. We had the band leader, Otis. He introduced the camera man, “Come on in the bar, we’re going to do a show.” And then we, the band, came down the stairs from the attic, “Hey, we’re going to do a Sailfest.” And then we recorded six tunes, put the video camera, went into the barn. And then sat on the stage at the end and just did a little salute, “Have a great Sailfest. We’ll see you next summer, hopefully in person. Everyone stay safe” and … Anyway, yeah, you can have more than one voice.
Kirill: Fantastic, yeah. You can apply this, not just to a data science career, but to your hobbies and to pretty much anything else you’re doing.
Christopher: Yeah, absolutely. Again, data science permeates data at a mental level, everything. Like two steps back, I always say, especially when I speak to business executives, every company today is a technology company, whether they like it or not. They’ve got to be involved on some level or they’re going out of business. I mean, it’s just that simple. So the translation for data scientists is there’s lots of opportunities to do interesting stuff around data and data science across all kinds of companies, all kinds of verticals, all kinds of businesses, all kinds of disciplines. So it’s pretty cool, exciting.
Kirill: Okay. In terms of this framework, one final thing to really drive it home. I’ve built the voice, I understood the voice, I’ve set up my antenna, I’ve created this mesh and is growing. What’s the end goal? What’s the end outcome of this framework? What do I get? How do I know that I’ve succeeded? What kind of criteria should I set myself and say, “If in three months I have this, then I’ve succeeded in building my voice antenna and mesh and I’m on the right track?”
Christopher: Yeah. I think the idea again, is to look for what your next career is going to be. These tools are all designed to help you track down what the next opportunity might be. As you follow people using your antenna and you connect with them using the mesh tool, you establish relationships, so when an opportunity comes up, either with a person that you know in your mesh or someone in their network in their mesh, you say, “Well, I’m doing something in your space. I see that you’re developing … whatever like plugin modules, so we can drive cars with a chip in our neck or whatever. I’d love to be involved in that activity. Let me know if there’s an opportunity or if you know anybody in your network.”
Christopher: At the end of the day, you want to let people know that you’re looking for opportunities and what your interests are, and keep them apprised of any work you’re doing is interesting, places where you speak, talks you give, charts you put together, code you write, applications you develop, relationships with other people in the thought leadership space in that particular area. It’s like playing the odds. It’s a numbers game at the end of the day. But that’s the way you get ready to be offered an opportunity or to have someone identify an opportunity for you to move into your next career. And then the one after that. So, you can set a target for …
Christopher: I always say to people, once you get comfortable in your job, you start looking for your next job. I learned that really at IBM. But it’s like, think about what you want to be doing in six months or even a year. Build out the mesh of people in that space and keep in touch and let them know as you get closer to wanting to transition that you’re looking for an opportunity. Again, it’s all about who you know. Yes, that’s just play the odds and continue to work it.
Kirill: Wow, fantastic. Fantastic, Chris. Thank you. Thank you very much, very insightful. I think it can be very valuable. I actually down here as you were speaking. I thought that this framework could be helpful indeed. Not just for people in data science, but I know a friend of mine that is currently searching for, what is their calling in life? With this COVID and the job is being disrupted and sitting at home and not having this ability to interact socially. You get to thinking, what is my calling, what is my purpose in life? I have a friend like that. I want to share this framework with her. She is not in data science, she has nothing to do with data science, but definitely this will be very helpful for her. So thank you again for sharing this. I’m sure this will be useful for people listening to this podcast and also maybe people that they know and love and care about that they want to help out as well.
Christopher: Yeah. Well, thank you. I think that’s exactly right. I think it can work across any discipline or any skillset. The other thing I would say just in closing, I guess, is that my general advice certainly to data scientists, but to anyone using this toolkit is threefold, chase the maelstrom, find the chaos, go for the mayhem. It served me well for 45 years.
Kirill: What is maelstrom? I haven’t heard that word before.
Christopher: Maelstrom is like a whirlpool going down in a base or whatever.
Kirill: So chase the maelstrom, find the chaos and explore-
Christopher: Go for the mayhem.
Kirill: Go for the mayhem. Can you tell us, what does that mean?
Christopher: This is just me looking back on my multiple careers. Go where they don’t know what it is yet. Certainly from a data science perspective, there’s a lot of that going on. A lot of places where they don’t know what it is yet because then you can help create it, you can help design it. You can be part of a community that’s doing something new and interesting. You can in theory, be gainfully employed and remunerated for your work and paid for what you do. But you want to avoid stuff that’s been going on for a long time. The cool thing is there’s lots of new and interesting stuff going on.
Christopher: I would say, if you have an opportunity to work at a company, and this is with all due respect like I stayed multigenerational, whatever company, tech company, for example or a startup, I would say go to the big company for a little while because you’ll learn things in that setting that you won’t learn anywhere else. I mean, the stuff I learned at IBM, the way global multinational companies work, the rate and pace that they run at, the range of portfolio and services managing across a matrix, the level of quality of work that they expect, I mean, it’s pretty remarkable. But don’t do it for long, maybe do it for two or three years at the most. And then go somewhere where, chase the malestrom, go where they don’t know what it is, where some company is inventing something new.
Christopher: I wrote this kind of facetious note from a CEO to employees, again, on LinkedIn a few years ago called What, you’re still here? It would be a pink slip from the CEO to any employee who’d been in the company for three years. The tone was, how come you’re still here? Why haven’t you left to start a company we want to buy? Or why aren’t you working in the supply chain somewhere? Or why aren’t you at a partner doing something to help us go our model? Thanks for stopping by, you’re fired.
Kirill: Wow, wow.
Christopher: That’s my take anyway. It’s moving fast, it being the global economy. All that I was saying, there’s lots of really interesting stuff for data scientists to be doing and others as well.
Kirill: Absolutely, absolutely. Fantastic. Chris, thank you so much. It’s been a huge pleasure. This will be very helpful. Before I let you go, can you please tell us where are the best places for people to find you for you, your career and learn more about your work?
Christopher: Okay. Well, first I would say, please reach me on LinkedIn. I’m happy to connect. I’m a big fan of LinkedIn as a way to connect. Again, their mission, we’re at large, I don’t know if many people really know this, but they would say, I’ve taken two steps back, that they want to connect talent with opportunity at scale. That certainly resonates with me. So, reach me on LinkedIn. Follow me on Twitter @chrisbishop, that’s my Twitter handle. I have a website called Improvising Careers, you can follow me there.
Christopher: I have a travel log where I talk about all the interesting events that I attend and places where I speak. I have a YouTube channel with some videos as well. I have stuff on SlideShare presentations and videos. So, those are all ways to connect. Please connect, for sure. I’m happy to have a conversation with any and all of your listeners about how to apply these tools.
Kirill: That’s really great, that’s really great. Of course, don’t forget about Chris’ course, Feature proofing your data science career. It’s on LinkedIn as well, on LinkedIn Learning. You can find it there. Very cool. Chris, one final question. What’s a book that you can recommend to our listeners?
Christopher: Oh yeah. I’m going to hold this up even though people aren’t all watching. But my recent book actually after Ruchir Sharma book is called More: A History of the World Economy from the Iron Age to the Information Age. The author is Philip Coggan. He’s a writer for the Economist Magazine. But I encourage anyone, and data scientists, especially because, data on some level, has been part of how global economies are created and evolved and morphed and developed for literally thousands of years. So, for those of you who are either history buffs or are into economics, it’s written in a very entertaining style. But he talks about, again, how economies have morphed and changed and driven by technology specifically and data science as it relates to various aspects of technological evolution. So, yeah, that’s my current read.
Kirill: Fantastic. More by Philip Coggan, check it out. History and data science together, I love it, I love it. Chris, once again, thank you so much. It’s been a huge pleasure having you on the show. I’m sure this will help lots of people.
Christopher: Well, thank you, Kirill. It was my pleasure to be on with you. Thanks very much for the invitation. I really appreciate it.
Kirill: There you have it everybody. Thank you so much for joining us for this podcast. I personally enjoyed this conversation. I like to think that in life, I know what I’m passionate about, I know what I want to do, but this was still very useful to me because it helped me, gave me a framework to identify well, hold on, what if I want to have more voices? What if there’s other things that I think I’m passionate about or I’m trying out and how would I go about investigating? Or how would I discover additional things, in the first place, that I’m passionate about? Even this exercise showed me that maybe I’m passionate about psychology and maybe that’s something I should look into further. Moreover, it’s a great framework to share with friends and colleagues and those who might still be discovering themselves. I have at least one person in mind whom I’m going to talk to about this framework.
Kirill: I hope you enjoyed this podcast as much as I did and got some valuable takeaways from here and maybe even some actionable steps. And as usual, you can find all of the materials for this podcast, including a link to Chris’ course and his LinkedIn, where you can connect with him at the show notes at www.superdatascience.com/379. That’s www.superdatascience.com/379. Make sure to connect with Chris. He has a very cool photo in LinkedIn where he’s holding a bass guitar. At the same time, if you know anybody in your life, not necessarily in the space of data science, but in general, who is searching for their passion, how to build their career and the next steps to take in this professional space, then send them this episode. It’s very easy to share, just send them the link, www.superdatascience.com/379. On that note, thank you very much, my friends for being here today. I look forward to seeing you back here next time. Until then, happy analyzing.
