Cracking the Job Hunt Code: Strategies Beyond the Conventional-- with Nick Singh

  📍  if you could travel back through time and change something in your past, I think a lot of us would want a  chance to help us succeed in the future. Unfortunately , there are no second chances and your best bet is to be as prepared as possible for that first impression.  I'm talking to an expert in first and second chances.

Nick Singh is an authority on interview prep for the technology field. interned as a data engineer at Google and worked as a growth engineer at Facebook. He's the co-author of ace, the Data Science interview and founder of data lemur.com, A way to practice hundreds of real SQL interview questions for free.

Nick, I think most people don't even know how to get in front of a huge tech company like Google. A few weeks ago, Logan Kilpatrick from OpenAI mentioned that he applied 500 times. 

How did you get your proverbial  foot in the door in Google? And what should Logan have done to maybe just shorten that to  a hundred applications 

 First of all, Ray, thanks for having me. Let's go help Logan. I  know Logan in real life, shout out Logan. let's figure it out. He's working  at OpenAI. So he ended up doing fine. But if you're ever in that situation, you're trying to apply, you're not able to break through.

I'm a huge fan of cold emails and cold dms, which means not just hitting easy apply, not just applying for the job on the company website, but  reaching out to the hiring manager, the recruiter, sending them a pitch, emailing them more about yourself and why you think you're a good person for the job.

Sending them a LinkedIn connection request, asking them from informational interview. Putting yourself forward. Rather than just being a random person that gets screened out in the ats. 

you asked me about how did I get my break at Google?  In the Bay Area there were all these intern open houses where companies would throw an event to meet in interns who are interested in working at their company in the future.

One of those  companies  was called Google's Nest Labs. Now here's the problem. I was interning at Microsoft in Seattle. And this was an event in San Francisco. So I just said, shoot, let me just RSVP to the event and then not show up just so that they have my email, their resume on file.

I know that the RSVP is to collect my resume. A month later they're , Hey, sorry we missed you. But you seem like a smart person. You were interning at Microsoft. Do you wanna start the interview process with Google? And I'm , hell yeah, I do.

They sent me this email and I'm , hell yeah, I want to interview at Google. And then it was crickets and then I sent another email.  Thanks for reaching out to me. How do we get started?

Nothing. I sent a third email. Hey I'm still super excited. Are you guys still hiring? I want to start the interview process. At that point, the recruiter says, Hey, sorry we dropped the ball. We still really like you. Let's schedule a first interview. So through this process, I learned two things.

First of all, I interviewed,  I RSVP to an event I didn't even show up at, and that was  a channel to interview at Google's Nest lab. So that told me , whoa, there's so many more ways to apply than just simply online. And the second painful thing I learned is persistence  really matters because you'd like to think every recruiter out there is  not dropping the ball and on top of things.

In that situation, I had to advocate for myself. They were already interested in me and for whatever reason, I wasn't getting my interview scheduled. So that was  me  being annoying and falling up three times saying, Hey, you guys wrote to me first.

So, this game of applying to jobs, it's not as straightforward as just hitting easy apply. There's a lot of things about being persistent and trying to look for other avenues and trying to  meet people and talk to people that can lead you to interviews rather than just thinking of these companies as black boxes where you're throw on your resume and you just pray for the best.

A lot of people don't realize the power of events , and you really found a way to hack the meeting by making sure that you were in the mailing list for the event.  That's why I love the book that you wrote.

I love the first and the last chapter.  You talk a lot about those  interview hacks, what are some of the  top misconceptions that people have about these technology interviews? 

  One thing I learned very quickly through  the interview gauntlet and a lot of top tech companies, Microsoft, Google worked at Facebook, all that.

What I learned was you can  prepare. People will make up all these stories on how you can't prepare, oh, they can ask you anything. It's not possible to prepare, or, if you got good grades in school. You don't need to prepare because your GPA can carry you through or your base knowledge can carry you through.

What I quickly realized through interviewing at these companies is interviewing is a real skill. It's disjoint.  Interviewing for a software engineer or data science position is related to software and data science. But to say, mm-hmm. doing well in schools, doing well in your exam, making really good open source contributions. That's all great, but that's still a different skill.  Software engineering interviews are a lot more structured than data science interviews.

They're testing data structures and algorithms and there's a lot of resources out there to help software engineers with their interview, including the book Cracking the Coding interview, which inspired me to write my book a science interview. There'll be a lot of commonalities and a lot of inspiration.

Her books helped me prepare for these job interviews, data science is a lot more open-ended.

So preparing there is a little bit harder.  Because data science and data analytics is so much more open-ended, this idea that you can't prepare is even more ripe.  People are even more saying, Hey, between statistics and product sense and SQL and coding and machine learning and some math, who knows what they're gonna ask me?

Why even prepare? Let's just show up and wing it. So that's the biggest misconception. I'm always trying to tackle that. Hey, even if it seems ask anything, most companies tend to ask similar things, and there's common patterns, strategies, and frameworks you can use to tackle some of these most common interview  questions 

 I'm not a data scientist. So the formulas were  a little much for me, but I really love what you talked about in the 10th chapter, which is about product and some of these  Practical tips for getting your foot in the door.

Do you recommend that people begin with a smaller company or just go straight for  Microsoft and these huge companies? 

  I think you should apply everywhere, especially in today's technology climate where a lot of these big companies have hiring freezes or some startups are struggling to raise funding.

If you're early in your career, apply it everywhere and see what happens I don't worry about that kind of thing. I loved my time working at startups.  I worked at Safe Graft for nearly two years where I held a variety of data product and evangelism roles. Having worked at these big companies, they were awesome. Free foods trait perks are great, but I also love the startup life.

That's why I'm running data lemur.com as a startup because that's where my heart lies. But, I think wherever you can start your career is great. I think the bigger thing is the kind of effort you put into that first job or the effort you put into executing on those projects spending extra time outside of work to learn.

That's  probably a bigger driver success than what company you work at, or what brand name you have on your resume early in your career,  you can outwork any disadvantages in the first few years in your career. 

 Even with  AI and some of the things that we've seen with the market shifting with people having to come back to the office, I saw in the latest stack Overflow survey that developers are still in demand, they're still making good money, and that there's still, a lot of opportunity, especially in these fields like data science and machine learning, ai, they're still hiring a lot of people and they need that pipeline of new talent.

So it doesn't matter that you just got outta school, there's still a lot of opportunities. So let's go ahead and get into this concept of  cold emails, which you have a lot of examples in your book. There's  templates, a lot emails that you've used in the past.

So ta talk a little bit about how 

 that works. 

The whole point of cold emails is the idea that. You don't have to know the person you're writing to, whether it's a LinkedIn dm, email, a Twitter dm, whatever platform you use. The point is you can write to people you don't know.

Most people aren't gonna be annoyed if you're a stranger and you're writing to them, as long as the message you keep is relevant, friendly shows that you've put in some effort. That's what cold emailing is all about. How do we get noticed and how do we form personal connection?

 Resume is great, but a lot of people don't read resumes. When you apply online, you get filtered out through the ats. Maybe no human even will read your resume. But if you send a person, a DM and you personalize it and you say, Hey Ray, I saw you do LinkedIn learning and I.

Do a lot of online education. Here's a link to my Udemy course and here's my link to my LinkedIn course. How do I, can I talk to you about online course creation and job opportunities at LinkedIn that would catch your attention, right? Or if I said, Hey Ray, I saw your CO on GitHub is super awesome.

I  made my own courses too, and I would hope to be working on LinkedIn. How do we make this happen? That well, he cash your attention compared to, Hey Ray, I want job. Every day I get dms from people , hi Nick, I want job.

I like that, the titles that you have here,  on the book, ex Google and Microsoft intern interested in working full-time at Periscope data people  perhaps you and me who have a, a large  follower audience or if you're a c e O, you are just getting slammed with a lot of stuff all day.

That attention getting title. Yeah. And very friendly and very focused, and you're highlighting, your advantages that you have in there. That's really good. I love that about the 

 examples that you have. 

You gotta show that you're respond worthy, and again. I wanna emphasize this cold email tactic is not  a panacea, it's not gonna work every time.

It's  a numbers game. I  got rejected, maybe one in 10 emails. People will respond back to me even after I'd, been able to intern at Google and run a little startup in my college days. Still most people ignored me. But the thing is, you only need one job. You only need one internship.

Even if nine in 10 people ignore you, if one in 10 get back to you and you're , Hey, you seem pretty interesting. Let's talk, let's do an informational interview, or let me refer you to somebody we're making moves, then we're making something happen.

Second thing I wanna cover is  we're talking after you developed a base of portfolio projects and why you should be respond worthy.  I don't want to tell people , if you have no work experience and no projects and nothing to show off, you can't expect a cold email or DM to work.

It  works when you put in that work previously, especially if you've done work in public, whether it's a medium article,  you've put  your code on GitHub and you have a popular repo. Maybe you have a portfolio project, maybe you've made a  data visualization that went viral on Reddits R data is beautiful subreddit.

As long as you have something worth showing off, something you can  point to, then it works out. If we don't have that let's not even worry about cold emails. Let's not put our hopes on emailing random people cuz it's not gonna  work. 

A lot of people do focus on the relationship part,  I think they miss that aspect of, that person doesn't know you, so you have to immediately give  relevant information

I like the way that you said, Hey, Ray, I love your course. I've been doing some things myself. Here's some links that you can watch. How do you get. Into course development,  that's somebody that I want to help I can go check out their work, I can verify the quality.

I'm not gonna recommend anybody that says that they wanna build a course if they don't show me anything. The people that I'm going to recommend  are going to want that as well. What have they done? Where can we see samples of their work? So that is the critical thing.

What are perhaps some other ways that you can. Differentiate yourself from others. You mentioned, GitHub, what else? 

 People forget to put their work out in public.  they have a, have repo but it's marked private maybe it's public, but they don't have a read me or maybe their read me is a bunch of random stuff on how to install the package rather than something,  I'm used by 50,000 people and I help automate DevOps.

When you're showing off a GitHub link, they're looking for what is this that you've linked me to? Is it public? If I needed to poke into it, I can poke into it, but 99% of people are Lacey. We're not gonna poke into it. 

We just need to see this exists. And oh look, it has 200 stars and oh look, he, the developer claims it's used by  people. We're good to go. 

 Let's say you're a data analyst, you don't know source control, you don't use Git. Can you have a public dashboard using to blow public to blow public free. It's a drag and drop tool. 

If you're applying to any bi job and you say,  I know Data Viz saying, data viz is way different than saying, Hey, I love the 2022 World Cup. So I built a dashboard that summarized all the soccer stats that lets you explore team by team performance. Here's a link in case you happen to love soccer.

That will still get my attention, even if I don't love soccer. I'll still be  let's see what this person's made. Having a link and making sure that link is thoughtful. It's not a link to a read me with a bunch of gobbly gook.

It's not a link to a code. It's linked to something that will catch someone's attention. Whether it's a beautiful read me, if it's an infographic, whether it's a Tableau dashboard, something I can  do something with, that's one big thing for a portfolio project

Try to align it to your dream industry. Or  align it to your passion, right? Maybe this umbrella term for it is passion, right? Whether you're passionate about a certain industry or you're passionate about music or you're passionate about sports, if you can make some project about your passion,  sound like a more passionate person about  life in general, data in general, tech in general.

 I did a project on hip hop music called Rap Stock io, which was  a stock market for rappers.  Think of it like fantasy football, where instead of betting on football players, you could bet on and long and short different artists.

A stock market for rappers. I was really passionate about Drake and passionate about music, when in interviews talk to me about rap stock,  my passion for Drake and hip hop came out, but my passion for data and my passion for building  products came out.

And my passion for building for consumers came out. I'm excitedly talking about how I'm serving the music community and I'm talking about the numbers of my app.  I'm  talking you about how I built the features and how I built the algorithm for pricing each artist using Spotify data.

Suddenly I'm having a great time talking about these things. Cause I love music and I love data. But what you're getting out of this is, this guy seems like an interesting person. He's not passing.  He's doing things. Maybe this hip hop music project doesn't have a lot to do with my healthcare data science company.

clearly if this guy can get excited about music and do this maybe you can go analyze, insurance claims from a healthcare company,  wait what does one passion have to do with another? When you put yourself out there and you show you're just  a passionate person about data and technology and doing things, that's enough for most companies because most people aren't that passionate.

Most people just say they like data. Then you ask them, can you show me a data project? And they're  I have a group project from three years ago where I did a little bit of the work. I don't have a link for it. And. I forgot what I did because it was three years ago. It was a group project.

There's something that people can relate to in terms of excitement that, if we were talking dryly about, data regression, it's not the same as, I built this thing that I really loved.

I could tell how excited you were just from hearing you talk people can find things in you that reminds them of themselves. When I was reading about your history with rap stock, you mentioned you used to DJ in high school. Yeah, I used to DJ in high school, I'm not into drag, but we now have something in common. That's a better conversation than me just talking to you about, data models

 after we finish a job interview, it's oh, we like that guy. That guy used to be a DJ maybe that doesn't have to do with healthcare , but somewhere between talking about hip hop, talking about betting, talking about music, talking about DJing, talking about data and pricing and stock markets.

Usually the person on the other side. There's some way to connect, cuz there's a lot of DJs out there, there's a lot of people who love music. There's a lot of people's, a lot of people like the stock market.

Even if you don't like all those things, they like data science, which is why, my project was a data project on how do we price these artists and  facilitate this marketplace of betting on rappers. Which is hey, even if you don't like any of the things we just talked about, at least you like data science because you work at a healthcare data company and I like data science too, it's able to elevate your presence and elevate who you are and.

A lot of this job stuff is also  likability and  personality

I love that you said that you have to know your target audience whether it's a resume or a LinkedIn profile, should be targeting not the data engineer who's working on the project, but the recruiter that's a different audience.

What do recruiters , they like to see numbers related to something Yeah. Real. 

People come back at me and they're like, Nick, you didn't use deep learning in your project. Or Nick, really, you think it to blow dashboard can make moves, why you didn't write 7,000 lines of Python code?  How are you gonna ever stand out? And I'm , look, by the time we're in an interview and you ask me technical questions, gimme a sequel interview, you gimme a Python interview, fine. I'll ace those things. Getting my foot in the door with the hiring manager or the recruiter getting my cold emails read, my cold dms read, they're not happening because I wrote 7,000 lines of Python code.

They're happening because they're , oh, I love music too. Let's see your project. Or Oh yeah, I work at Spotify and you did a music project. That's great. Let's talk. Or hey, I work at s and p 500 or nasdaq, you made a little stock market at rappers. We literally make the stock market. let's talk.

Little connections like that can really help you out. People always think to be impressive. You had to use some really crazy deep learning algorithm. And the bar's lot lower at the end of the day, people are all humans.

As long as you can tell a good story and you have something to show off, which is why I go back to the Tableau dashboard. Having a beautiful little thing like that is far better than 7,000 lines of code on GitHub with no explanation, no visual, no read me, and it's  just there. 

You're talking to perhaps a recruiter who's maybe on his or her 50th candidate that they've looked at. They've been watching Tableau analytics. Every person that I've interviewed in this show I've been able to connect with in some way.

We had Cassie Kozyrkov last week and I was talking to her about Sadie St. Lawrence, because they both shared an interest in psychology, neuroscience  you can relate to others. 

Let's say that you've already got your foot at the door, you've got your first job.

What should your plan be after that?

It's  funny how much just caring a little bit can be a competitive advantage. Cuz it's really easy for people to get jaded in a lot of products, in a lot of work.

Not being jaded and just trying to find things that keep you interested in the job or keep you interested in the vision is a competitive advantage. And that might also mean gaslighting your brain into being  you know what? Health insurance claims is not the most fun thing, but let's go get excited about it.

 Let's find a way to wake up every day that we're excited about rather than on Monday morning we're thinking,  what's happening for the weekend? How do I take PTO off? How do I do the least amount of work before my manager yells at me?

Surprisingly a lot of people are doing that to just get by. 

do a good job at work, care about what you're doing. 

We always think of our  careers as too rigid because, people worked at the same company for so long, and now we can see the way the world's moving.

People are job switching faster than ever. People are switching their titles faster. What we called a bi engineer might be called a data scientist now, and they might be trying to be an AI engineer. We live in a really real weird world right now where you can   job hunt, especially across data.

Think of your career as more flexible when I was in school, I did not like writing. Now I have a bestselling book. Aday Science Interview sold 27,000 copies.

It's the number one best seller on Amazon. And I don't like to write, I avoided all liberal arts classes in college because I didn't wanna write. I thought of myself as a bad writer and maybe I'm extreme to go from not liking writing to now making my living as a writer.

Even though I went to school for engineering look at the kind of growth you can do if you just put your mind to something. Look at the kind of weirdness life affords you.

We always think your living is your nine to five. And then there's  things like being a course instructor posting on LinkedIn and writing a newsletter and hosting a podcast and making an open source thing and consulting. Your career is much wider than you would've thought because in today's day and age, We can do all these things while holding a nine to five.

I don't know if that was possible in the past. Cause we didn't have social media, we didn't have blogging. We didn't have ways to make side incomes the way  we do now. 

There's also a lot of things that you can do where you're at. Good managers will allow you to pursue something that is related to your job, ask to allow you to pursue, a side project related to your jobs.

When I get to that rut in my job, I try to look for things that are going to make me really happy about what I'm doing. This series of live streams has been part of that. 

 I started working, with GitHub  almost 10 years ago, and that became  a partnership with GitHub, an internal project within the company that changed how.

Everybody did everything. that was just me thinking,  there's a way that we can use GitHub to host our repositories. And I'm going to see how this would work started using it, other people found out and then they got into it it eventually became  a legit internal project in the company.

Eventually you can become  the hero of the republic just because you had that, ability to look for what else you could be doing that would be super exciting 

 I love that story. One thing you mentioned was doing it on your time. A good boss maybe says, oh, you can have some time to do it, but in practice, If you really wanna make moves, you have to find extra time to do these kind of things. Most bosses don't really like you putzing around too much on random things.

That's right. But that's kinda where some of the best things happen

If you just tell somebody an idea, a lot of times they can't visualize it. If you show them the idea, then they can see how it's beneficial. This entire stream is something that  I'm gonna see a lot of things come from it.

Some things are longer term projects. You have to have that kind of attitude. Let's try something. And if it doesn't work, you try something else, but that's what makes it exciting.

How did I go from working at Facebook and engineering to now being a content creator, a data person, and  making my living through a book? It's  because back in 2018, as I was transitioning and leaving Facebook to join that startup, I said, you know what?

I learned about how to get jobs at Big Tech. Let me share what I know on LinkedIn. And at that point I had about a thousand connections. Five years later, I have 150,000 connections on LinkedIn. I just started putting up career posts and blog posts and  posting on LinkedIn thinking, eh, maybe this will help someone.

Slowly and slowly got more likes and more connections and more followers. When Covid hit that's when I realized, oh wow.

I have 50,000 followers now. Have put out career advice for two and a half, three years. I bet you if I could put it all together, we'd have half a book or maybe a book already. This idea of, the career is a lot wider. You can do things small and then see what happens is literally how I got to the book.

I didn't think of myself as a writer. I didn't like writing. But with 50,000 followers on LinkedIn being able to write consistently for two and a half years, it gave me the conference to do this thing. Just like you might not have gone and done this whole GitHub initiative, but you did a little things and it slowly snowballed.

You did things in your side time, became this, authority on GitHub. For the world on LinkedIn learning take these  small things seriously, try a bunch of different things and keep at it consistently.

 It's always been easier for me to prove something as opposed to talk about something. If you have the project that shows what you can do, that's a much easier conversation than trying to, have a presentation about what you think it could be.

If you have 50,000 followers, that's something that you can do something with. If you have a project about wrap stock io you can leverage the things that you've done and to something else. And opportunities will,  drop in your lap from things that you've done before that you didn't even know had an  impact.

That's literally how when I pitched my co-author Kevin Ho, he was a former data scientist at Facebook. He had worked on Wall Street as a quant.  He is not a writer by any means or  ever thought he would do this. When I pitched him,  it came from seeing his believing and just let's try it out.

Let's both put out our stuff in public and see what happens. And, after six months of doing that and having built up these small little content pieces, small little tips, small little blog posts, we saw, we have traction. It's not just useful for other people. Seeing as believing is not just for other people, it's for our own self to build confidence

if you're scared to make a startup, you're scared to post content, you're scared to do something, you're just take baby steps.  You yourself will see it and believe it and get that confidence. It's not just for others. 

 Let's switch gears a little bit and I want to talk about this world of AI that we're all getting into.

In the latest Stack Overflow survey they took a deep dive into AI and found out that 70% of developers are already using or plan to use AI tools in their development process. How do you think this is gonna impact hiring? Is that gonna change how people are doing their jobs?

Think of Chat GPT, as a better stack overflow. Even after Stack Overflow is invented, there's still a reason we're asking software engineers to write US code during an interview.

Interview, try to test problem solving skills that things like Chat GPT can't do. Understanding problem context and thinking a few different strategies even being able to prompt Chat GPT  to solve a SQL interview question requires a little bit of problem solving on your end. We're not at that point where I can just give it a cold.

Tough SQL interview question and can write the code entirely.  Half the issue is how do we  frame the question, how do we frame the prompt? There's still a lot of room for coders and data scientists to know some of these fundamentals and be ready to do them in interviews.

Though GitHub co-pilot's awesome and we have Chat GPT and they have all these other AI assisted code writing tools, just  how Stack Overflow didn't kill the software engineering interview, I don't think that Chat GPT will kill the SQL interview or coding interview for any time  soon.

In your 10th chapter you talk about your business sense of product. That's maybe something that becomes a little more important.

 Maybe over time syntax becomes less important, but those high level problem solving skills or understanding the business context, asking the right questions what's the right thing we should even be doing at a high level?

That's the bigger thing, I think. All these AI tools aren't there yet. So maybe over time the interviews will be less syntax heavy unless framing the question important and  framing your answers

If you were hiring your own data science person, what would be some of the kind of questions that you would want to know from those  people?

What I would love to know is what's the hardest. Bug you faced or hardest thing you had to do in data science and what was hard about it? I like asking this just to see someone's depth. To see okay, what did they get?

Get into? How do they overcome this hard problem? What was hard about it?  If in the back of my head being , it seems  really easy.  I don't think you know what you're doing.

It's a great way to see who's done a lot of classwork versus who's  done real life data science and real life data work. If you don't even have a good war story, that makes me really skeptical you sat there and did some quizzes and maybe you got a certification.

Now you talked about the hardest technical thing. Can you talk to about the hardest people thing? I like this better for people who are more senior, who are managers, inevitably you're gonna find a PM who tries to block you or gonna find some data engineers who don't give you the right data or this or that.

I like asking that to get a sense of your behavioral interview skills, your project management skills. It's easier way to have that conversation than me asking a typical behavioral interview question that's really lame. Tell me about teamwork. Tell me about a time you worked as a team.

It's yeah, that's tell me about a challenge more so I like to see oh or relate it to that hard technical thing oh, that's great. Thanks for talking to me about this really hard tech technical bug. While we're on this topic, did you have any people issues too, or  how did you deploy to prod.

And usually they're  no, we didn't get the quota project. And I blocked it. And I'm , okay, who blocked it and why? And  how'd that go? And what was there? And then we start talking about the people stuff. So I like asking that as one thing to do. And then the second thing is I, again, for data people, I like asking about what's the,  gnarliest data set you had to clean.

People don't like cleaning data sets. Some people who have done only academic stuff, they work with really clean data from the get go. I like asking and hearing about people who have to solve really gnarly data cleaning issues. And then I also really like data, people who have some more software engineering skills.

So if someone's, telling me , oh, you know what, I  scraped my own data set. Or, you know what,  I had to step in and be a little bit of a data engineer because I noticed there are problems in the pipeline. So  cleaning it. Was not just a job for me, it was  going above the stack and  cleaning the source data cuz the data engineer had messed up or the warehouse was broken or blah blah blah.

That's another thing I like asking about cause it's okay great hearing you step into other people's roles or going upstream, especially at smaller companies where data infrastructure is not very mature and practically speaking, you as a data person will have to kinda do some data engineering type work or data warehouse type work or database administration work at some point.

It's my way to filter that out as well. Tell me about data or yeah, and if they're just , oh, everything's handed to me, it's really clean and I just have to build a model on top of it. I'm , okay, that's fine. But especially in smaller companies, that's  just not a reality. And so much of our work is just in cleaning the data that, it's okay, but.

It makes me a little suspect or I'll at least try to double down. 

  Yeah, it definitely goes back to what you said about, under,  getting people who have built stuff and understand what  happens when you have things, when you run into problems. It's really, I think, what separates great developers or people that understand what this job is about.

Understand that everybody I don't know is happy when things are going well. What happened when, everything, went down. What did you do then? That's exactly, kinda, I think what proves a person. So I love  all the resources you have available to people. So besides the book, you  have this , fantastic website at this data lemur.

So maybe you can explain why you picked a lemur out of all the animals that you could have picked and talk a little bit about some of these things that you have here. Resources for 

 people. Definitely y'all can check out Data Lemur as a way to practice for SQL interviews for free. Hundreds of questions asked by real tech companies. We can click on that Facebook one, for example. So this is a real interview question asked through data scientist at Facebook, and you get the input data and we have a coding environment where you can  run the code, if you're logged in, you can submit the code too and see the solution, get it graded

Nobody just writes a book and then also builds  ara to run sample codes. So this is  the same questions that are in the book or is something different?

There's hundreds of questions on the website. Many of them are not in the book, but some of them are in the book. I tell people in the book, you wanna go practice it for free? Let's go run the code online, it's a companion or buddy to the book, but I think the book is still disjoint and it has a lot of material and content that's separate from the website.

I try to run them as two different businesses. Why did I call it Dave Lemur? I love that movie. Madagascar as a kid. Oh yeah. There was Julian. He likes to move it. Move it. That one. So I like that. Of course, I knew my website had to be named data something cuz I saw data bricks.

And data dog and  data camp. So I was, I'll be data something. So I was , let's pick an animal. And I looked, Datadog's taken data, cat's taken, so I saw data Lemur was not taken. So I was , all right . I think it's a fun little thing. So that's the story of data  Lemur. 

People can find your book just on Amazon.

You should write a book, just take chapter 10, make it an entire book the first few chapters are really good for not just data scientists, but anybody trying to get into the industry itself. You have such good experience with that.

Let's go ahead and see if there's any questions that I wanna pull up from people . Someone says:  you can look and apply everywhere, but still look at the companies and make sure the culture's a match for what you're looking for.

So how much emphasis did you put on stuff  culture match for yourself? Like when looking for these companies? 

  I don't worry about it too much. I worry about it if I have an offer in hand, but before that I'm just trying to get an offer. If this company is doing something really unethical or weird,  fine. Amongst your biggest technology companies where there's thousands and tens of thousands of technical people. I don't even worry about culture match because, truthfully, it's so team dependent and org dependent no matter what Nvidia says on their site or LinkedIn says, or Facebook says about their org and team and culture.

 I'm looking at pay, I'm looking at who my team is, I'm looking at the work it is. I don't really worry about culture fit, to be Maybe at smaller companies it's something more noticeable because the CEO has a big impact. it's something that you becomes immediately obvious as you're interviewing for

 it's less important as you're just trying to get your foot in the door.

But more important over, the long term. If you end up in a team that has  a toxic person, that's something that you want to try to avoid. you can see that in the interview, but sometimes, you just gotta suffer through some jerks.

  A lot of my advice is couched for younger people. Or people who are  trying to break into the industry where I worry about these things later. But you're definitely right.

Crushing in your career. You've got a lot of experience then. Worry about the culture stuff.  98% of people I talk to are at the earlier stages, where're just,  let's break into Fang. Let's get a great name on the resume.

Money matters and having a great name on the resume matters.  If one person's not the nicest  This company works you a little harder, or this company, doesn't match your health insurance okay. 

 Stanley mentions here

connecting with people outside of your group is also really important., I think connections is the key to success in the long term as well. You mentioned that you ran into your partner, Kevin who, who wrote the co-wrote the book is the Data Science Interview with you.

That ability of people to connect with others is key. And I think sometimes developers especially tend to be more reserved, you need to train  yourself outta that.

Somebody's really impressed with your ability to do so many things, yeah, I think that you have to do that especially at the beginning of your career. You don't even know what you want when you're in school, right?

You're studying esoteric things that might turn into something. You might end up doing something completely different and you just never know. 

some people are maybe concerned about the, that quantity of work. I think that's really important to develop that ability to have the time to do things that are going to keep your skill level, in a certain place and taking that time to do other things.

One more question what are your general thoughts on AI and the industry? what do you think we're gonna be, are we all gonna be having jobs  in 10 years?

I'm always trying to be an optimistic person, but I do hear, AI, he's gonna take all the jobs. And I can see it, I can see that this being  a really powerful thing.

People are usually trying to limit ai, AI can't do this yet. And I'm  people said that for chess. People said that for go. People said that for writing. People said that for poetry. People said that for art. I don't think there's anything fundamentally unique or special or God-given in me.

It's just a question of when it happens. That part does worry me a little bit  I wanna believe that I can, do this high level business strategy thing and translate that into how that will shape our data strategy and AI can't do that. I think it's, it can happen at some point.

I just need to be set for another 30, 40 years while I'm working and, right after that? Take all the kids off. Good attitude. I got my money. 

 I'll say a couple things that I always mention when people ask me this question  think about what happened with something like Amazon and shipping.

When you ordered something from anywhere, it would take, four to six weeks for delivery, somebody. Amazon figured out how to make shipping way more efficient. And what did we end up getting? Not less shipping, but  more shipping.

And did we get rid of people that deliver stuff? No. Now we have  way more delivery people than we ever thought. If you look at a lot of old movies from  the seventies, they all thought we'd all be dead by now. That there was gonna be no food, that there was gonna be no space, jobs for anybody.

The future always a little bit different than what you think. Don't try to project so far that you missed the opportunities that are available now. I think, 

Don't try to , worry about macro stuff when at the micro level you are worried about AI and your job prospects in the future, but you don't have a portfolio project today.

I come across this all the time where it's oh, you're worried about the future of this, automating SQL coding. And I'm  have you done sql? Or how good is your sql? And they're  I'm not that great. I'm  of course the AI's gonna take your job. You're not even good at it, and you can learn it for free.

You can do the question. That's right. Data. So instead of thinking about CAT PBT and AI and how it'll affects sql. If you yourself can't pass a SQL interview today, then let's go fix that. Get a great job. We'll worry about these things in a few years when the AI's coming around, knocking at your door step.

Saying, Hey, gimme your SQL work. I can do it all right? Yeah. Until then, it's just yeah, 

 put your head down, continue to do the work be willing to move, be willing to adjust to the changes. Be the person that learns how to use AI better than everybody else.

You're gonna be faster, better, more efficient. Maybe you can teach it to others. And that could be exactly, the place that you swivel and put reposition yourself too. 

I work primarily with data people, AI people, software engineers, data engineers.

We are literally the industry where we can take advantage of it or create the future ourselves. Even more of a reason to be , let's put our head down and  try to think about how we can use these tools better or make our own tools.

We're literally in the position to do these things rather than worry about what's gonna happen.  You, yourself today can write a sequel writing GPT bot try to train something yourself to write sql. Let's go do that, rather than sit around and worry, oh, what  will, that's right.

Thank you so much for coming to the episode. I wanna mention that there's a few places where people can go to find out more about the show. There's a LinkedIn page that you can subscribe to and you can get notified of news and changes. Plus there's a newsletter with show notes, links from the episode.

All the links that I showed to your websites and your book and everything else is going to be in the newsletter with the show notes and highlights and all that sort of thing. There's a guide where you can find out who's coming up in the show and also a YouTube playlist and, Finally a way to listen through to this show as an audio podcast just in case you're traveling somewhere and you want an opportunity to, listen to this in your car.

Next week we have, Walter Shields a data expert with 25 years of experience managing data systems for startups and Fortune 500 companies. I know you put in a good word for him, Nick. And so he wrote sequel  A Quick Start Guide, and he has a lot of really top rated analytics courses on LinkedIn learning.

So we're gonna  take a journey through the world of data analysis and artificial intelligence, focusing a little bit more on the history innovations and some of the things that have already changed the world and that are coming soon. So thank you again so much for your time, Nick.

What a pleasure. I love your book. Everybody, even if you're not in data science, go check this out. This stuff in here is priceless. I would've never thought of your concept of cold emailing as well as the fact that, that you have  actual  letters and you show  how to write the letters that you're show people what you've done. You show them  your first resume, your second resume,  when you were at this company, what was your resume ? And even in reading what was in there, it's fantastic stuff. So thank you again for your time, Nick.

  I appreciate Ray and thanks for, I'm glad the book resonated with you and I hope oh, your audience, great audience check checks it out as well.