SDS PODCAST

EPISODE 379:

MAELSTROM,

CHAOS, AND

MAYHEM: GUIDING

YOUR DATA

SCIENCE CAREER

PATH

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

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

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

superdatascience.com/379. That's

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, 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.