SDS 359: Tackling Data Science Job Hunting, Interviews & Negotiations

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name is Kirill Eremenko, Data Science Coach and Lifestyle Entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today, and now let’s make the complex simple. 

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Kirill Eremenko: Welcome back to the SuperDataScience Podcast, everybody, super excited to have you back here on the show. In today’s episode, we had a fantastic chat with a brilliant data scientist. So Emily Robinson has had an outstanding career in data science so far. She moved on from a data science role at Etsy to a data science role at Datacamp, and now more recently, she’s taken up a senior data scientist position at Warby Parker. In addition to being a data scientist or senior data scientist, Emily actually also does a lot of appearances at conferences. So check this out. Since July 2017, so that’s under three years, in under three years, Emily has spoken at 26, that’s right, 26 different conferences or meetups. And on top of all of that, Emily partnered up with another data scientist, Jacqueline Nolis, and together they wrote a book, which is titled Build a Career in Data Science. How cool is that? So in this podcast, we dissected the different chapters of the book and extracted insights which you can already take away and apply to your career today. 

Kirill Eremenko: So here’s what you will hear about in this episode, what areas of data science anybody looking to get into the space should look into to understand how they’re going to bring value to data science and how data science is going to bring value to their career, how they’re going to be excited about doing data science. We talked about R versus Python, lots of references to Hadley Wickham there. We also talked about the five-company archetypes that they identified in the book and how pretty much any company that you’re applying for a position in data science for can fall under one of those archetypes. And that will help you better assess what is going on in that company or industry while just even knowing about these five company archetypes is already a great step in being aware of them. 

Kirill Eremenko: Negotiation, we talked a lot about negotiation. That is one of my favourite parts of this episode. We dove into a lot of interesting strategies, how you … And I actually picked up a few things for myself, how you can make sure you’re negotiating the right way, you’re not being bullied around by a company and that you are getting a remuneration that you want or maybe even beyond that. So very cool negotiation tips you will hear. I recommend checking them out. They can actually make a big difference in your salary. Maybe you can even get a bump of 10 to $20,000, and Emily gives some real examples of how she’s helped people and what results they’ve gotten. We also talked about finding jobs, where to find them, how to find them in data science. Those are just some of the things we talked about, lots of very exciting things. So if you’re looking to start a career in data science, to get into the space or looking to change jobs, or maybe you think you’ll change jobs in the space of data science in the coming future, this podcast is for you, and you will get a ton of insights from here. 

Kirill Eremenko: And plus, spoiler alert, Emily shared a special coupon for her book. So if by the end of the podcast you want to get her book, listen to to the end, because you can get a special discount for SuperDataScience Podcast listeners. On that note, let’s not put it off any longer and dive into this amazing episode. Without further ado, I bring to you Senior Data Scientist at Warby Parker, Emily Robinson. 

Kirill Eremenko: Welcome, everybody, back to the SuperDataScience Podcast, super excited to have you back here on the show. Today’s guest is calling in from Utah, Emily Robinson. Emily, how are you going today? 

Emily Robinson: Hi, thanks, Kirill. I’m doing well. How are you? 

Kirill Eremenko: I’m doing well too. I think in Europe they call this cocooning, cocooning in Spain, cocooning as in while the coronavirus is plaguing the world, people are wrapping up in their cozy homes to spend a few weeks in isolation or in quarantine. Is that what you’re doing in Utah? 

Emily Robinson: Yeah, exactly, so I actually came out from New York to stay here for a little while. This is where my parents live, and fortunately they have a guest house where I can just quarantine with my husband. And we have a little drop zone for any things they need to pass on, but it’s really beautiful out here, and it’s nice, because the house is a little bit isolated. So we can go for walks and other things and not worry about encountering anyone else. 

Kirill Eremenko: That’s really good, and yeah, it’s quite terrible, what’s going on with the coronavirus. It feels like the whole world has come to a stop, doesn’t it? 

Emily Robinson: Yeah, it’s definitely a little scary in New York now. So we’re recording this on the 17th, and they have shut down the bars, the restaurants. Schools are closed. Yeah, it’s a scary time that we’re living in and just trying to figure out, for me, how I can do my best to support other people, figuring out … I have some remote drinks planned with folks on Thursday. It actually for me, a lot of my friends, because I went to France for grad school and was in college in Houston, a lot of my friends are kind of scattered around the country and the world. So actually I’m pretty used to calling people and having to do long-distance friendships. Now everyone’s schedule is very free so definitely trying to make sure to stay connected with people, even if we can’t physically be in person together. 

Kirill Eremenko: Interesting. What does social long-distance drinks entail? 

Emily Robinson: I think it will be video calling in. And yeah, just everyone has their own drinks at their place. My parents supplied us for our two-week quarantine with seven bottles of liquor, so I think we’re well prepared. Yeah, but I think it’s just trying to find a way to casually catch up with some people and still see, definitely going to be making more use of video calls instead of just phone calls. 

Kirill Eremenko: Interesting. Would you describe yourself as an extrovert or an introvert? 

Emily Robinson: Definitely very introverted, so it is kind of funny that it’s not necessarily, I think, as much of an adjustment for me as it is for some other folks who are used to going out most days and seeing people a ton. We usually cook for ourselves. My husband is a great cook, so yeah, definitely very introverted, but still especially one-on-one chats or small groups chats to catch up with people. So even it’s interesting with introverts, it’s about where you get your energy. But as an introvert, I can often get energy from a one-on-one chat with a close friend. 

Kirill Eremenko: That’s good. 

Emily Robinson: Which is very different to me than if you dropped me at a cocktail party with people I don’t know and not sure what I have in common with them. 

Kirill Eremenko: Yeah, no, that’s true, that’s true. I also feel like I’m more introverted. I really feel for people who are more extroverted, because in this time when you can’t go out and meet people, that must be quite difficult for them. But hopefully it’ll pass soon. Hopefully in a few weeks or a few months, things will come back to normal again. 

Emily Robinson: Yeah, or people have kids too. I think that’s going to be very hard, if you have two working parents, or you’re a single parent, and you have your kids home. But I know I’ve seen people putting together some lists of what you can do inside, especially folks who don’t have a backyard, who are living in an apartment, how you can keep people active and keep the kids entertained and not interrupting your conference calls too much. Although, I think people are being very understanding too and that people are going to have to be flexible about when they work and that there might be interruptions in the background. I’ve definitely been heartened to see people recognizing that this is a very unusual time and being flexible and assuming positive intent. 

Kirill Eremenko: Yeah, and patient as well, patient with each other. Different people have different needs. All right, well speaking of staying at home, people can stay home and read books. And you recently published a book, good segue. Congratulations, this was your first book. 

Emily Robinson: Thank you, yes, this was my first book. And yeah, I’m really excited. I got my print copies about a week ago as a little bit in advance of other folks who had pre-ordered. So it was really nice to see all that work come together to a physical object. 

Kirill Eremenko: Nice, very nice. And for those who are listening in, the book is called Build a Career in Data Science, and I’m super excited to talk about it here, because I haven’t read the book myself yet. It’s something that I’m curious about. And from what I understand based on the book description page, it focuses quite a bit on combining those technical skills with the soft skills in order to really propel your career forward. Is that a good summary, or how would you describe your book? 

Emily Robinson: Yeah, so I think one way to describe it is having … There’s a ton of books out there for building your technical skills. If you look for, all right, I’m a beginner, and I want to learn to program in R or Python. Or I’m advanced, and I want to learn deep learning. Or I need to learn all about survival analysis, and you can find a textbook on that. So there’s a lot of resources available for that, a lot of tutorials online. But what we found lacking was really a comprehensive resource for all of the other things that are going to be important in your career, things like, how do I communicate with stakeholders? How do I write a good rèsumè? How do I deal with failure, if I’m encountering that? And so my coauthor and I, my coauthor is Jacqueline Nolis, we wrote this book to really serve as a comprehensive resource, because some of the things we found like writing a rèsumè, you can find resources online, but they were a bit scattered. Some of them weren’t tailored to data science. 

Emily Robinson: And then some other things, like our second chapter is on how does data science look at different companies, we just didn’t really find that many resources on that. You’d maybe find some people posting about what’s it been like working at data science at a startup but not something that looks, as we do, through five different company types and really comparing them with their pros and cons and helping you make a decision about what’s best for you. 

Kirill Eremenko: Interesting, and where did you get all this information? Is this from your and Jacqueline Nolis’s experience? 

Emily Robinson: Yeah, so some of it’s from our own experience, and I think this is where we complemented each other well. So Jacqueline has a PhD in industrial engineering. Her bachelor’s is in math. She’s been in data science before it was called that, working as a consultant. So that was a really great way to learn about data science at a lot of different companies. And for me, I entered data science now about three and a half years ago, first through a bootcamp, and then I went to Etsy and then Datacamp and now Warby Parker. And so I think we have very complimentary experiences. Jacqueline has been a manager, for example. But on the other hand, entering data science is different now, so I think it’s also good to have someone who’s entered a bit more recently. So some of it’s from our experience. We also, at the end of every chapter, have an interview with a different data scientist. 

Kirill Eremenko: Wow. 

Emily Robinson: That was really important to us, to get different perspectives. We also have blurbs throughout the book from folks on things like imposter syndrome or transitioning from academia to industry. Because we knew, while we have a lot of experiences ourselves, data science is a huge field, so we wanted to talk to people who are managers of managers, people at Google, people at startups, people who have transitioned to maybe doing more product management now, or they’re called engineering managers, but they’re still doing data science. And that I think really helped the book address people from everywhere from bachelor’s to PhDs from social sciences to engineering to humanities backgrounds, how they enter data science and their perspective on the topic of the chapter. 

Kirill Eremenko: Okay, got you. A lot of people have been hearing data science, and perhaps maybe even somebody listening to this podcast isn’t in the space of data science yet. How can we make it clear who would be interested in the field of data science? From what you’ve written in your book, from the research you’ve done, who would you say are the people best suited to work in the space of data science or who would find this field very interesting? To whom would you recommend to go into the space of data science? 

Emily Robinson: Yeah, I mean I think the advantage of data science is that, because it’s a newer field, and it’s pretty broad, there’s a lot of different backgrounds. So my background is in the social sciences, and I found that a very good fit, because I was in the quantitative social sciences doing research, and it’s a very similar process to data science. But I think thinking about, is data science going to be interesting to me as a career, a sign I think about is “okay, in my job right now or in my academic studies, have I been working with data? Do I enjoy puzzling through some numbers, making graphs, figuring out how to explain things to people?” In our first chapter, we define three different areas of data science, which I think can be helpful for folks to think about. The first is analytics, so this is basically, how do we gather and clean and display the information we already have? So for example, for a company that’s like “okay, how do I get all the sales orders? How do I graph that? Can I make an interactive dashboard for stakeholders? What kind of cuts should I do? Can I allow people to do it by different geographic areas and so on?” So really taking data that exists somewhere or gathering it and then just trying to figure out the best way to share it with people. 

Emily Robinson: And the second is decision science. So this is going beyond the data you have and using statistics and other methods to say, “What should we do based on this data? And how do we deal with the uncertainty that’s inherent in data?” So for example, I’ve done a lot of AB testing, and one thing there is you wouldn’t say, for example, “We have 100 data points for the control and 100 data points for treatment.” So the idea in AB testing is you randomly show half your site visitors one experience and half the other and see which performs better so maybe which converts more. And you wouldn’t say, “Well 50 out of 100 conversion A are converted in the control, and 51 out of 100 in the treatment, so 51 out of 100, that’s higher than 50 out of 100. We should launch the treatment.” You understand, you’re like, “Okay, but there’s uncertainty here. How can we use statistics, for example, to figure out how long we should run our AB tests for and how to deal with the data that we get.” So decision science is really around let’s make some decisions. What should we launch? Should we launch this treatment? Where should we put our new store, and so on? 

Emily Robinson: And finally we have machine learning, and I think that’s often what people outside of data science think about when they think of data science, because machine learning, for example is how does Amazon make those recommendations that you see when you visit a product page? Or how does it, when you search for Harry Potter on Amazon, how does it know or how does it decide whether to show you the books or the movies or merchandise and so on? And so that’s a third area that we really think about. And so machine learning is all about, how do we take some data that we have and then use it to predict what is going to happen with new data? So for example, in the recommendation algorithm, what they’re trying to do is predict. Okay, if I recommend these things, I want to recommend the things that you’re most likely to buy, so I got to be able to predict that. Or we’re going to … Credit agencies use it to decide whether to grant a loan, because they’re trying to predict are you going to default on this loan? And what’s the probability of that, and how much is their risk tolerance? So I think it’s helpful to think about those three areas and think about, all right, is there any one of those that’s really appealing to me, or is something similar to what I’ve been doing already? 

Kirill Eremenko: Okay, very cool, very, very, interesting. And from what I’ve seen, people from all sorts of backgrounds have been able to successfully start and build a career in data science, from mathematics to social scientists from physics and engineering to arts and crafts and things like that. Everybody leverages their kind of background. Would you agree with that? Would you say that every type of background is conducive to having some sort of different thinking in the space of data science? 

Emily Robinson: Yeah, absolutely. I mean I know, for example, someone with a background in English, and I think communication is a really key skill in data science. And one thing that humanities teaches you is how to communicate well, how to write well. I mean that being said, certainly if you have a degree, for example, in English, and you haven’t studied that much math or statistics or programing, you will need to gain those skills. But I would certainly think that … Don’t discount all that work you’ve done. And the other part is, if you have been working for a while in a field, maybe you’ve been working in marketing, that’s a huge asset, because a big part of data science is having domain knowledge and gaining that domain knowledge. And if you have that from a previous career, like maybe you worked in retail, and you can go and do data science for a retail company. And you’ll be so advantaged to have that domain knowledge already from your previous experience in the field, even if it wasn’t as a data scientist in that domain. 

Kirill Eremenko: Totally agree. And let’s say I’ve decided to become a data scientist. I have some experience, as you said, in marketing or some other field. What are my first steps? What is your recommended approach for someone to tackle this? Do I quit my job and start applying for data science positions right away? 

Emily Robinson: Yeah, I mean I think it does depend on how close your background is. So for example, if you’ve been working as a marketing analyst, and so you’ve been using Excel spreadsheets, you’re very comfortable with numbers, you’ve been looking at using Google Analytics, stuff like that, I think the best thing to do there is to try to figure out, how can I do what I’m currently doing but maybe start to incorporate programing, for example? So almost every data science job, you’ll need to program, and the two most common languages are R and Python. So maybe you try, instead of making a graph in Excel, see if you can try making it in R or Python. And there’s a ton of online courses and textbooks and other ways you can use to get up to speed on that. So I think if you can, and you’re in an adjacent job, I really do think the best way is to start doing it in your current work, because that way you can continue to get paid for it. Hopefully it makes you work more efficient. Maybe you can produce things you couldn’t produce before. That makes your boss happy, so that’s a really great way. But say you can’t really incorporate it in your current job. 

Emily Robinson: So our third chapter is all about, how do I get the skills? And really the two other big options are doing a master’s degree and doing a bootcamp. Both of those options, you do have to have some background already. So for example, the bootcamp I did, Metis, you had to do a take-home programing assignment, answer some stats questions, because they’re not designed to take you from zero to 60, from nothing to a fully fledge data scientist in three months. They’re designed, okay, you have some of these skills already, business you need some dedicated time to improve it. So for example, for me, I had a good background in statistics and in R, but I hadn’t done Python, and I hadn’t done machine learning. So that was really helpful for me to get that. But with all of that you have to weigh the cost, obviously. You have to weigh the time you have. 

Emily Robinson: But I think it can be helpful to start, I would say, with some low-cost options, to start with things like trying it in your work or taking a free online course before saying, “Okay, I’m going to commit $30,000 to a master’s degree,” and then find out you don’t actually really enjoy data science. Because there is certainly a big hype around it, and it is a great field, but that doesn’t necessarily mean that everyone is going to take to it and to really try find out in the lowest cost investment and time investment first if this is something that’s appealing for you. 

Kirill Eremenko: Speaking of education, I heard on one of your talks, that one of your lecturers, professors at Rice University was Hadley Wickham. How cool is that? 

Emily Robinson: Yeah, that was really great. So if any of your listeners are more Python, Hadley is probably one of the most prolific R programmers who’s made- 

Kirill Eremenko: Yeah, we had him on the podcast a few episodes ago, actually. 

Emily Robinson: Yeah, I heard it. It was a great episode. And yeah, so I got really lucky to have him as a professor. So he had redesigned some of the courses to use R. I mean this was back in 2011, 2012, so this was … ggplot was around. I think we were using plyr. There wasn’t dplyr yet. So it was funny to see. I think reshape2, how it’s all evolved since then, but that really got me started on the right foot, and I’m very grateful for that. 

Kirill Eremenko: Nice. And now that you know both R and Python, I think this would be useful advice. For somebody starting out from scratch, not knowing either of the languages, which one would you recommend for a person like that to begin their journey into data science? 

Emily Robinson: Yeah, so there’s definitely pros and cons to each. I would say, from a purely … Okay, looking at jobs, what’s more common? And I would say Python is more common. And certainly if you want to do machine learning, Python is more common, especially in production and machine learning environments. That’s not to say R can’t do it. So my coauthor, Jacqueline Nolis actually has put R in production, given talks about that with her wife, Heather, on how they’re using R in production. And it’s getting hit 5 million times a week or more, but that being said, you’re often working with engineers on that. And engineers might already know Python or are more comfortable learning Python than R, because it’s more similar to other languages. So just from a purely numbers perspective, Python is a bit more popular. 

Emily Robinson: That being said, I actually still use R, and all the teams I’ve worked on have used it. And one of the things I really enjoy about R is I do think it can be more friendlier for people who are coming not from a programing background, who this would be their first language, because a lot of it’s human readable. So dplyr, for example, you could show that to someone who’s never looked at code before, and they’re probably able to get it, because it’s things like filter, group by, summarize, things like, okay, I think I can guess what this code is doing. And there’s a really, really great community in R as well that’s very friendly, very welcoming to new people, great at answering questions. I really love the R community on Twitter, and there’s … I think you’ve had Gabriela now twice on your podcast, right? 

Kirill Eremenko: Yeah, Gabriela de Queiroz, yeah. 

Emily Robinson: Yeah, so she, as your dedicated listeners know- 

Kirill Eremenko: You know the podcast very well, surprisingly. 

Emily Robinson: Yeah, [crosstalk 00:25:44]. 

Kirill Eremenko: Yeah, I actually spoke to Gabriela two days ago. We were talking about our upcoming conference and how she’s got this new project. She started R-Ladies.org, and now she’s got… 

Emily Robinson: Inclusive AI. 

Kirill Eremenko: AI Inclusive, yeah, Aiinclusive.org, very cool project as well, very excited about that. They just launched their second chapter, I think, somewhere. 

Emily Robinson: Yeah, so I really found, and Python has Py-Ladies, but I’ve really found the R-ladies community to be great. So yeah, I mean I think to sum it up, any of your listeners trying to think, what I would say is maybe try both. And then you try both, and you’re like I still have no preference, and you think community style is important to me. I just want as many jobs open as possible, maybe go with Python. But definitely pick one and stick with it for a while. I would not recommend trying to get to an intermediate level in both. I think you’re much better served, and I think Hadley mentioned this maybe on your episode as well, much better served to just start with one after experimenting with both, perhaps. Then stick with one, really learn that well, and then if you need it later, you can pick the other up. 

Kirill Eremenko: Yep, absolutely. It was so interesting talking to Hadley about him learning Python. He just did it for fun, but he still doesn’t use it. He still uses R. 

Emily Robinson: Yeah, I do think also, if you’re going to do visualizations, it’s funny. I think there’s like 3 million different ways in Python, packages that are trying to emulate ggplot2. So I think even diehard Python people will recognize that ggplot2 is a great graphic library. And I think most folks, the Tidyverse is a really nice way to draw together all the data analysis process. And yeah, and the last thing maybe is take a look at the type of jobs you want, because for example, if you’re more researcher type, often people from academia, or it might be more common there. So you could try, if you have an idea of these are the type of companies I’d like to work for or these are the type of positions, maybe just take a look and see what they ask for. Do they ask for R or Python? And that could also be a deciding factor. 

Kirill Eremenko: Absolutely, that was exactly what I was going to say next. And not just companies, also industries, like you said, research or maybe pharmaceuticals, medical. They are more on the R side of things. That’s where R got a lot of its development, as I understand. Whereas if you’re going more into, I don’t know, industry and things like specifically, I don’t know, mining or banking or other things, they might have specific preferences. It might be more of an industry standard to use one or the other. 

Emily Robinson: Yeah, exactly. 

Kirill Eremenko: Got you, okay, so we’ve covered off that important question, R versus Python. We know what areas of data science. What I’m really curious is you said in your book, you described five different companies. Why five? How did you pick these companies? How do they differ? I’m just curious, does that encompass all of data science or most of data science roles as you can begin with would fall into one of these five groups, five types? 

Emily Robinson: Yeah, so certainly it’s not all. I mean of course there’s always the exceptions, and we chose archetypes of companies. And so the five ones we chose, one, it was massive tech companies. So if you’re thinking of Google or Microsoft, those types of companies or Facebook, and then we have a company that’s like an established retail, so for example, Bed, Bath and Beyond or Best Buy. We have a government contractor types so maybe Boeing, a late-stage tech startup like Lyft or Twitter and then a small, new startup. So as we say, hundreds of startups you’ve never heard of, that have died out since then. And the reason we chose them is at the end of that chapter we’ll say, one, it’s very possible the company you’re looking at, it’s clear which one it maps to. It’s like you’re looking at a small startup, you’re looking at Google. But even if not, what we talk about is okay, what are different criteria that companies vary on? So we say, “Okay, think about how much bureaucracies are going to be?” So for example, a massive tech company or a government contractor is going to have a lot more bureaucracy than a small startup. What is the job security? So a small startup is going to be a little bit riskier, for example, than a defense contractor or a massive tech company. 

Emily Robinson: How much mentorship are you going to get? Do you want to work with a pretty established team, where it’ll be among a lot of other data scientists, or are you going to be the first data scientist they have? And so rather than being like definitely every company you look at, you can automatically put it in there, it’s trying to help you think about what should you be looking for in a company, and there’s not necessarily a right or wrong answer. People have different preferences. For some folks, it would be very important to have job security, maybe if they have a family, or it’s just they’d rather have that than necessarily working at the hottest startup. But for other people, they want to have a high growth opportunity, and they want to wear a lot of different hats and have a lot of control, for example, and not deal with bureaucracy. And they’ll be attracted to a small startup. So that’s how we thought about picking those archetypes as some of the most common ones where data scientists work but also showing the spectrum across these different things you might be thinking about when deciding what type of company you’re interested in. 

Kirill Eremenko: Fantastic, fantastic. Well on that note, let’s talk interviews. Do you have any advice in your book on how to approach data science interview? 

Emily Robinson: Yes, so we have a whole chapter on that, Chapter Seven, and we also have an interview appendix at the end of the book, where we gave 30 example interview questions and the answers that we would give and the notes on why we gave that answer. Because some of them are technical questions, where it’s like we gave this answer, because it’s the right answer. But some of them are behavioral questions, where there’s not necessarily a right answer, but we talk about things. Okay, with the behavioral question, tell me about a time when you had a disagreement with a teammate. And we talk about, all right, in general you want to approach behavioral questions this way. What was the situation? What did you do in response? What was the outcome? But also for that question, we say, for example, “All right, what you don’t want to come across is I resolved it be getting them fired. Yeah, now we never talk again.” It’s like they’re asking that, they want to see. We talk about, what are people looking for when they ask these types of questions? 

Emily Robinson: But yeah, in our interview chapter, I mean the first part of what we do, we also just describe what it’s like, because I think there is some mystery around data science interview processes. It certainly is not as standard as, for example, engineering interviews have become, software engineering, because it’s newer. So one thing we do warn you is you may, one company may have you program only in SQL. Another company may ask you to invert a binary tree on the whiteboard. Another company may ask you in-depth statistics questions so just mentally preparing for that a bit but also giving some advice, for example, on case studies. So a lot of companies do take-home case studies, and we talk about what type of things should you be thinking about when you’re … What are they looking for? So we say things like they’re looking for … Can you work with messy real-world data? Can you structure analysis? Can you produce something useful? And then we give some advice on how to accomplish those goals and show them that you can do that. Yeah, so there’s certainly a lot that goes into interviews, but we try to demystify the process a little bit, give our advice. And then we also have a chapter on negotiating an offer and how to handle offers, because I think that’s something that’s really important for folks to think about. 

Kirill Eremenko: Interesting, so could you please elaborate? So let’s say a job says you will be paid $500,000. Let’s say $80,000. How do you negotiate that? What do you do? At which point do you negotiate it? 

Emily Robinson: Yeah, definitely. So we talked earlier. How did we come up with the stuff for the book? So actually this was where my academic background helped. So I have a master’s in management specializing in organizational behavior. And so one of the things I studied was a lot of research papers on negotiations, including job offer negotiations. 

Kirill Eremenko: What a useful thing to study. 

Emily Robinson: Yeah, exactly. 

Kirill Eremenko: Everybody should study that. 

Emily Robinson: Yeah, I do think it was very … It was a great thing to study. I studied teamwork. There’s a lot of interesting, useful research out there. If anyone wants to get up to speed quickly, HBR often has … It’s for a more general purpose audience, but it’s by the academics who were doing this research. So that’s a good source rather than necessarily going to the research papers themselves, which may be kind of long or specific to one small topic. 

Kirill Eremenko: Harvard Business Review, right? 

Emily Robinson: Yeah, Harvard Business Review is a good source. 

Kirill Eremenko: They also have these little books that you can get, booklets even with top 10 articles or papers on team management or leadership. Or like you say, negotiation, they have them at airports. I think at the Dubai Airport they have them in one of the shops. Every time I stop by, I pick one up of the, pick up one of them, and it’s really useful to read them. 

Emily Robinson: Yeah, I haven’t picked them up before, but I definitely believe that. But yeah, so a quick primer on negotiations, so one thing we really recommend is try to avoid, if at all possible, giving them a salary number. Because some companies will ask you, “What are you looking for in terms of salary?” Or this is now illegal in some places like New York City, but in places it’s not illegal, they may ask, “What’s your salary now, or what’s the previous salary?” 

Kirill Eremenko: You’re saying try to avoid telling them [crosstalk 00:36:07]. 

Emily Robinson: Exactly, because you’re giving away … The problem is, when you give away a lot of your power, so for example if you give them a number, let’s say they were thinking there, so to back up, most companies will have a range. Pretty much all companies have a range for a position, and that’s certainly bigger companies. Startups may be flying a little bit more by the seat of their pants. But bigger companies will be like this position is going to have … To get the headcount approved, it’s like, all right, the salary is going to be 120 and 160,000. And it’s usually a pretty wide range, because that gives … People may have different experiences or education and so on. But they have that number, and if you’re like, I’m expecting, I’m looking for a salary of $100,000, they may not go all the way down to that. But they may be like we would’ve thought that we would’ve put them at 130, but now we can give them 120, and we know they’ll be thrilled with that. 

Emily Robinson: So one way you can push off that question is you could say, if it’s early in the process, you could say, for example, “I really want to learn more about the position and how I’m a fit for it before discussing those numbers.” Or you can say, “I’m really going to look at the whole package,” because this is a very important point. Remember, salary is just one part of a job offer. So for example, is their 401K matching if you’re in the US, retirement savings matching. Is healthcare covered? Do I have remote work options? That’s important to me, or a flexible schedule. And are there other perks, for example? Do they offer a gym reimbursement or catered lunches? And so to really try to think about all of those parts and not just get focused on the salary number, so first try to push off answering that. 

Emily Robinson: Second, let’s say you get to the offer stage, and they call you, and they say, “We’d like to offer you $80,000.” What do you do? So the first is definitely remain enthusiastic throughout this process and thank them and really emphasize that you’re excited to work there, because that gives them a good feeling, and you want them to believe, for example, if you negotiate for some things, and they give it to you, that you will take the offer. And we definitely do recommend, if you ask for something, and they give you all of it, you should be planning to take that offer. So that’s something that’s important, is to remain enthusiastic, thank them and then ask- 

Kirill Eremenko: Otherwise, if you don’t take that offer, you will leave that hiring manager heartbroken, and he’ll take it out on the next person. 

Emily Robinson: Exactly, and you might also burn some bridges. The data science world is fairly small. Maybe that person will be working at your dream company in a couple years, and they’ll remember. It’ll seem like you were stringing them along, maybe for example, to try to get when you were negotiating with another company, right? 

Kirill Eremenko: Yeah. 

Emily Robinson: Because that’s a big component, your biggest piece of leverage is a competing offer. So if you have an offer for another company that’s higher, so for example actually, I know someone who had five offers from a company, and one of them offered- 

Kirill Eremenko: Really? 

Emily Robinson: Yeah, so he was in a great position. 

Kirill Eremenko: From one company? 

Emily Robinson: Sorry, from five different companies, five different job offers but around at the same time that they were all open. And his favorite company, they offered him 200. So he’s pretty senior. They offered him $200,000 a year, and he was saying and they said, “This is what the market research …” so on and so forth. 

Emily Robinson: And he’s like, “Those are good data points, but I have four other competing data points, and two of those are at $250,000 a year. So those are the other offers.” It’s rare to be able to negotiate that much, and it’s partly I think because this company was a smaller company. This was the first time they were hiring a principal data scientist, so it wasn’t like there was all these other people that had a pay band established. But he was able to get a huge raise because of that, because he could very much say, “Look, my best alternative if I don’t take this offer is I get paid $250,000, so I get paid 50,000 more than you’re offering.” So that’s a huge piece of leverage, if you have it. 

Emily Robinson: Or for example, a current job, so I talked to someone else who was offered 5,000 less than their current job, which is not a lot, but they also got good benefits at their current job. And normally I say earlier in the process don’t disclose your salary expectations, but at that point I told them to definitely disclose it, because they could say, “Look, I’d have to take a pay cut to come to this job.” And most companies will understand that you don’t want to do that, and so they were able to get a $10,000 increase in their offer. 

Kirill Eremenko: Wow, wow, very cool, awesome. 

Emily Robinson: Yeah, definitely keep in mind, again we go obviously much more in depth in the book, but it’s very normal to negotiate. No company should pull, as long as you’re polite, you’re not like how dare you give me such a horrible offer. I will never speak to you again unless you give me 20,000 more. Unless you’re very unreasonable, companies expect you to negotiate. Often you can get at least a 5% increase in salary. Remember to think about what are the other benefits. Some of them are easier than others. Like for example, it’s hard often to get a 401K match difference, because that’s just set at a company level. But you could use that as leverage. You can say, for example, “My current company has a 5% match, and you don’t offer a match. And that mean effectively right now, my salary is 5% higher than my base salary looks.” And so you could use that as a leverage point to maybe get a higher salary or a signing bonus. Or ask for a performance review in six months instead of a year, where you’ll be able, if you’re performing well, we’ll revisit your salary. 

Emily Robinson: So there’s lots of different ways that you can negotiate, and again we do talk about some specific tactics as well like, for example, remaining enthusiastic and packaging things up so it doesn’t feel, for example, they answer one thing, and you’re like actually I have this other thing I want to talk to you about. And actually I have this other thing. And just to really try to stay polite, stay enthusiastic, remember that it’s normal to negotiate. And it’s certainly uncomfortable for people, but as my brother once told me, if someone said, “If you stand on one foot for a minute, we’ll give you 5,000 more dollars,” you’d be like absolutely, even if it’s a little uncomfortable. So it can be a huge payoff, and it can really compound over the course of your career, even just a couple more thousand dollars in salary. 

Kirill Eremenko: Yeah, absolutely. Those couple of minutes or hours that you’re in those interviews, they’re deciding your future for the next year or two or five. Might as well approach it very carefully and thoughtfully and strategically. And I love that you have this chapter in your book. I can totally get behind that advice. Don’t ever go first in any sort of negotiation. There are very rare circumstances where you should disclose your position first. You should always listen to the other person or company, whatever it is. And you should get them to say what they’re offering. And this is for any position which basically, if their market already exists for a position or a service or a product, and there’re kind of ranges in the world, everybody knows that this position for this specific role would probably get paid between somewhere in the vicinity of 80 to $140,000. Well let them go first. Let them tell you their range, which might be 120 to 160, or it might be less or more, whatever, because then you might’ve had something, like in your example, something much lower in mind. And you save yourself that whole situation where you’re missing out, because you just didn’t know their range from the start. So definitely don’t go first. 

Kirill Eremenko: And the other advice you gave is fantastic as well. I’m sure you have plenty more in your book, so it was a really cool idea to include that in your book. 

Emily Robinson: Thanks, and one last thing I want to add, definitely prepare. And except for very rare circumstances, like for example maybe if it’s 25% lower salary than you thought, in that initial call where they give you an offer, thank them and say, “I need a few days to consider this.” And really take that time to think about it, to prepare what you’re going to say, maybe even write a little bit of script, because you want to lay down what’s important to you. For example, if they can’t move on salary, I’m going to ask about a signing bonus, or I’m going to ask about stock options rather than trying to do that in the moment, to take a little bit of time to think about it and to prepare yourself for the negotiation. 

Kirill Eremenko: And then what’s your template response if they did say something that they said like 80,000 or in any case? Even if you’re aiming for 100, they say 120, is it worth trying to push it even further and asking for 130 just to check it out? Maybe their range was higher? 

Emily Robinson: Yeah, absolutely. Do not base it what you’ve been paid previously or what you thought. Once they give you their first offer, if it’s higher than you were expecting, once they give you their first offer, still negotiate. There’s almost always room to negotiate on something. So definitely, I had another friend who got … What was it? A 40% raise, and they actually still negotiated for another $5,000 and some more stock options. Because you know what? Why not? And yeah, so definitely keep going, but if you do get one that’s very disappointing, like I said, with some rare circumstance it’ll be hard for them to, for example, give you a 30% salary bump. So if you’re really disappointed, and you’re like I don’t know if it just might be out of their pay range, I would maybe say something like we write in our book like, “Thank you so much. I’m really excited about this opportunity and the work I’d be doing at company Z. But I want to be honest that the salary is a fair amount lower than I was expected. I know that in New York City, the market rate for someone such as myself with a master’s degree and five years of experience is in the range of X to Y. What can we do to get the offer more aligned with that range?” 

Emily Robinson: If it’s so off, that’s why I say maybe doing it in the initial call rather than waiting a week, because honestly it might just be possible, even if you’re an excellent negotiator, they’re just like our band is just this. HR, we can’t move that number. And your only hope may be that they can classify you as a more senior employee, so you’re in a different band. 

Kirill Eremenko: Yeah, got you. And to your point that negotiating, even if it’s higher than what you expected, still negotiate. Why? Because if you haven’t yet disclosed to them what you’re currently earning, and if you’re not planning on disclosing that, that’s bonus to you. But basically they, for a company if somebody negotiating, and you don’t do it arrogantly, but you do it in a nice way, you do it like you say, that you’re excited for this opportunity, for them you’re not lowering your value by negotiating. You’re actually increasing your value. They’re like that’s right. If we’re going to have to pay that person more, then that means they are worth more. They value themselves more. Maybe we should value them more as well. You already have the offer for whatever, maybe 120 or whatever. Just try to be very polite and considerate, but see if you can bump it up a bit more, always a good practice, fantastic. 

Kirill Eremenko: Okay, so some people might be thinking we skipped, jumped the gun a little bit. We went straight to the interviews. Do you have any advice on actually getting the interview? What does a data scientist or data scientist to be professional, what do they need to do? What’s your best advice for getting those interviews, print out 100 companies of your rèsumè and send it to 100 companies? 

Emily Robinson: My gosh, yes. We have a lot of advice, of course. So I’ll just briefly talk about a pre-step even before applying. One thing we have another chapter on, Chapter Four, is building a portfolio. So I think this is a big benefit if you don’t have previous job experience in data science or related analytics field, is how do you show you can do the job. And a really great way is having a GitHub with personal projects so showing that you can code. I think we recommend it’s even better if you can blog about one of them, because that shows you can communicate. And also they’re more likely to skim through a blog post than try to ready your thousand lines of code. And so that can be a really nice way to be like, okay, even though I haven’t done it for a company before, I’ve gathered data. I’ve worked with messy data. I’ve cleaned it. I’ve analyzed it, and I’ve put it through to a final product. So maybe that’s a machine learning algorithm. Maybe that’s some visualizations. Maybe that’s a interactive web application. So that’s a really nice way that can help you stand out. 

Emily Robinson: But okay, you have that. I think this has definitely been talked about on your podcast before, but a network is huge. A lot of job opportunities come from either people you know or meeting people at meetups or conferences who have a position at their company available or reaching out on LinkedIn. And your network is easier to develop, honestly, if you’re already in data science. So how do you develop it before you’re in the field? One thing anyone can do is go to meetups. So I really love the … In New York we have the New York Open Statistical Program Meetup, and we have the R-Ladies Meetup, and I go almost every month to that. And I’ve met a lot of people through that, and it’s a really nice way to meet folks at a bunch of different companies. But let’s say maybe you don’t have local stuff. 

Emily Robinson: So we include in our book, so [Mark Mellin 00:50:25], who I’m a big fan of, he wrote a blog post about how the most effective messages that reach cold reach out, people he doesn’t know are ones that combine a thank you with an ask. So thanking him for his blog post maybe on something or a podcast recording he did and then following up with, “Hey, can I maybe talk with you for 20 minutes on this topic?” And so you’re showing that you’ve done their research on them. You’re not just spamming everyone on LinkedIn. You have already looked at any public work they’ve written about this topic, and then you’re asking them something that they haven’t answered already. So that’s a nice way to kind of start building your network there. But in terms of rèsumès and cover letters, we have a chapter on that. There’s a lot of advice. That’s something that’s easier to find online, for example. We really advise, keep your rèsumè to one page. You want someone who only has 10 seconds to skim it to find the key facts right away. So that might be where you’ve worked before, your education, a link to a portfolio, the skills you have and so on. You want them not to have to parse through three pages of a rèsumè with tiny font that’s just like all these small bullets and trying to figure out what are the key points. 

Emily Robinson: Write a cover letter, if they ask for one. That can be a way that … You don’t want to just be reiterating your rèsumè but telling a story, saying why am I interested in this company, showing that you’ve researched them, because that can make you really stand out. So there’s a lot of different advice here, and a final thing I will say, and this was [inaudible 00:52:07] Chapter Five interviewee is especially for your first job, don’t get caught up in the data scientist title, because there are so many other positions with different titles like data analyst, quantitative analyst, researcher. 

Kirill Eremenko: Insight specialist. 

Emily Robinson: Insight specialist, where you could use data science, and it might be easier to transition into those jobs than, for example, a data scientist job. And especially also don’t get caught up in I have to go work at Facebook or Google or Airbnb as a data scientist, because those companies do have the luxury of being more picky, because they get tons of applications. And they can be great places to work, but so are a lot of other places. And think about what you want to do. So [Jessie Mosspack 00:52:56], who we interviewed in Chapter Five, she said, for example, for her, she’s done a lot of nonprofit data science. And she’s like “I’m fine working with messy Excel spreadsheets. I don’t need to be doing cutting edge deep learning, because to me it’s much more important. I want to see the impact of the work that I’m doing, and I want to help causes that I care about.” So think about that for yourself to. How do you want to make a difference? What’s important to you in data science jobs and not just applying blindly online through 100 rèsumè portals where, to be honest, it’s probably going to go into a huge pile and maybe not be seen. 

Kirill Eremenko: Fantastic, fantastic, and I completely agree with your points about going to meet people. I’ve heard the statistic that 70% of hires worldwide happen behind the scenes through connections and through networking and go to a meetup, go somewhere where you can catch up with people. I think you lead by example, Emily. I was looking at your LinkedIn, and it says all the conferences where you gave a talk recently or in the past couple of years. It’s a huge number of events that you’ve been to. It’s probably in the several dozen, right? Is that about right? 

Emily Robinson: Yeah, I think it’s maybe up to like 20 now or something, and I started, let’s see, now two and a half years ago. My first meetup talk was either July or August 2017, and then my first conference was January 2018. 

Kirill Eremenko: And just listeners of the podcast, I really recommend going on Emily’s LinkedIn and go where it says public speaker and experience. You will see that the 20 places she’s been to, it’s crazy how many conferences from the R Conference in New York to a Data Professionals conference in Taiwan, Women in Analytics Conference at Facebook, Booking.com Headquarters speech. That is really cool, and I’m sure you meet a lot of people along the way. You probably have met hundreds of data scientists. 

Emily Robinson: Yeah, and it’s also great, there’s a snowballing effect. So for some of those conferences, so I know, and this is actually a benefit that most people won’t have. My brother, Dave Robinson is also a data scientist. And through him, I met his coauthor, Julia Silge, who’s now a engineer at R Studio. And this was maybe two years ago, but she, when I was working at Etsy, Lukas Vermeer, who heads up Experimentation at Booking had reached out to her to talk about how Stack Overflow where she was working at the time does AB testing. And after they finished talking he said, “Do you know anyone else who’s doing AB testing at other companies that I could talk to?” And so she introed him to me. We chatted about how Etsy does it and Booking does it, and then actually through him, he recommended me to speak at CXL Live, which is a sort of experimentation like conversion conference. When I spoke there, Ronny Kohavi, who’s over at Microsoft, saw a video of my speech, my talk and invited me to speak at internal Microsoft conference. And [inaudible 00:56:16] saw my talk at CXL and invited me to speak at their Conversion conference in the Netherlands. So just from this one connection through Lucas, who has been like a great sponsor for me, I was able to speak at all these different conferences. 

Emily Robinson: And it’s been really cool and get to travel too. So now I’ve traveled to the Netherlands, London and then hopefully in Spain in November to speak. Yeah, so I’ve met a lot of different people, and it’s been a really cool experience. I really enjoy going to conferences. R Studio is one of my favorites. I went for the fourth time again this year, and it’s a great way to catch up with people from all across the country and the world, who I’ve gotten to know through conferences and then kept up with through Twitter or Slack channels. 

Kirill Eremenko: Very, very true and very excited also to announce to everybody that we spoke with Emily before the podcast, and she’s most likely happy, most likely going to come to our DataScienceGO event in October. And I mean most likely, if this coronavirus situation resolves, and everything is good, hopefully it will in the next few months, then would be super thrilled to host you as a speaker at DataScienceGO. How are you feeling about that? 

Emily Robinson: Yeah, I’m so excited. It’s interesting, these different conferences, their focuses, for example. And often, for example, at R Studio there might be some more career-focused talks, but of course there’s also a lot of technical talks and different things. And I was really excited with DataScienceGO that you have whole tracks developed to some of that nontechnical stuff, but that’s really important for people to know and often can make a huge difference in their career, is understanding things like communications, applications to companies. One thing I might talk about or given previous talk about is this building a portfolio. How do you do that, or how do you expand your network? I gave a talk about that to my data science bootcamp. But yeah, I’d be really excited. I’ve heard great reviews of the conference. Seems like a super friendly conference and with a lot of knowledge being shared. 

Kirill Eremenko: Awesome, thank you for the kind words, and yeah, would love to have you there. I think it aligns very well with what a lot of people want to get out of the event, the networking, building a career, I think your book aligns really well with that and would be very cool to present it to them. On that note, we’re actually running out of time slowly. So I wanted to ask you, what is a final idea that you want to share with people listening, people who are excited about a career in data science but maybe a little bit apprehensive about all the work that they need to put in to really build a career, not just successful but a fulfilling career for themselves? 

Emily Robinson: Yeah, I think a couple things is my final take-home message. One is this is your own journey, and data science will look very different for different people and to try not to get caught up into I need to be an AI researcher making a million dollars at Facebook or chasing after status. And think about, but what’s important to you? What do you care about? How do you want to make an impact? What kind of data science work do you want to do? The second thing with that is don’t listen to the gatekeepers. It’s really frustrating to me when I see things like you have to have a degree in math. Or what do you mean, you don’t use the steep learning library? You’re not a real data scientist. I think that’s really just a very damaging point of view. 

Emily Robinson: And I would say basically we define you’re doing data science if you’re using data to help something, to make decisions, to communicate something. That doesn’t mean necessarily anyone can walk in and be hired as a data scientist right away, but there’s a lot of ways to do data science and to start practicing. So it can be certainly really daunting, and I would recommend rather than, for example, starting with taking a ton of online courses, maybe take one and then try to build a little project and try to build it around something you care about. So one of my most recent blog posts is on Pokemon. 

Kirill Eremenko: I saw that, yeah. 

Emily Robinson: Yeah, so I was playing Pokemon, Let’s Go, and I wanted to build a team. You get six Pokemon, and I wanted to choose what are the six Pokemon types that will be the strongest against the most defending types. And so I did a little simulation in R to do that. So don’t necessarily worry about I got to do a project that is super professional or will get me hired, because a lot of the skills I show off in this blog post are helpful to projects at a company. So try to keep it fun. Try to do things that are interesting to you. And my final thing was find community, because it’s so helpful to be talking with other people, whether that’s other aspiring data scientists, whether that’s the people with the career you want to have in a few years, but you don’t have to go it alone. There’s great, friendly people either at local meetups or on Twitter that are really there and happy to help you. And those relationships both can help your kind of mental health and building your technical skills but can also of course help in your job search, as we’ve talked about. 

Emily Robinson: Yeah, so I think those are my biggest piece of advice around it. Yeah, I mean data science is a great and growing field. There will always be more to learn, so just keeping that in mind, I’m still learning every day, trying out different stuff. So don’t feel like I have to wait until I know everything before I can apply, because you’re never going to know everything. So once you feel like you have … Okay, I have a decent foundation and stats and some programing. Try applying to some jobs. Let the market tell you whether or not it thinks you’re ready after you design your rèsumè around some best advice and use that rather than waiting until you feel like you know everything, because you’re never going to know everything. 

Kirill Eremenko: Fantastic, fantastic, thank you for amazing advice. And also I wanted to say Emily’s blog is Hookedondata.org, all one word, and check out the Pokemon combinations matrix. 

Emily Robinson: Yeah, I also have some career advice on that blog, including two on building your network, if that would be interesting to you. 

Kirill Eremenko: Fantastic, so don’t get the book. Read the blog, and maybe later get the book, if you really want to. But yeah, no, fantastic book. I’m glad you and Jacqueline got together to write it. It has been needed. People want to get into the space of data science, often don’t know why. We don’t want the space of data science to be missing out on talented people from all over the world. Huge thank you to you for that, and I’m sure it’ll help many people. Before I let you go, what are some of the best places to get in touch with you or follow you and the career or the book, even? What are some places online? 

Emily Robinson: Yeah, so definitely the best place is Twitter. So I’m Robinson_ES, so that’s where I often … It’s a pretty professional Twitter, so usually I’ll be posting or retweeting data science stuff, some about the career. And Jacqueline is also fairly active on Twitter. Hers is Skyetetra. I don’t know how to pronounce this, but it’s S-K-Y-E-T-E-T-R-A. We also both have LinkedIns, but I don’t update it that much, although you’re welcome to find me on LinkedIn. But yeah, Twitter is definitely the best place and also my blog for any future posts. 

Kirill Eremenko: Also the book, where can people buy the book? Is that available on Amazon or somewhere else that they can purchase it already now? 

Emily Robinson: Yes, so it’s available on Amazon, and I think by the time this comes out, it will be out of pre-order on Amazon. You can also find it on Manning, either through my short link, which is Datascicareer.com. Or my coauthor, Jacqueline made one, which is Bestbook.cool. And that will link you to Manning. Those are also good, because there’s a 40% off code you can use, which is podsuperdataSC19. That’s P-O-D-S-U-P-E-R-D-A-T-A-S-C-19, and that’s good for 40% off. And Manning does it, all the physical books come with an ebook. So on Manning’s site, you’ll see those are the two options. You can buy ebook only or physical plus ebook. Or if you order through Amazon, there’ll be a code included inside your book, so you can get the ebook copy as well. 

Kirill Eremenko: Fantastic, fantastic, okay, thank you. Those are great places to get your book either on Manning or on Amazon. I’ve just, while you were describing that, I already placed my pre-order on Amazon, looking forward to my copy. I love when a guest comes on. Why I get it on Amazon is because when a guest comes on the show, I get the book. If I like it, I’m excited about it, often I don’t have time to read the whole thing, but I’ll read parts of it. And then I’ll post a review. I like to post my photo with the book. I think it’s pretty funny. I take a funny picture usually with the book. 

Emily Robinson: Yeah, well that would be great, although you’ve given away, Kirill. If I don’t see a review from you in a couple months, [crosstalk 01:06:10] like the book. 

Kirill Eremenko: Got you, got you, yeah. On that note, thank you so you much for coming on the show and sharing all your insights and knowledge with us, very, very exciting. I’m sure this episode and hopefully your book will help lots of people around the world get into the space of data science. 

Emily Robinson: Yeah, thank you so much for having me. And folks do have followup questions after they read the book or the blog posts, please feel free to reach out to me on Twitter or LinkedIn. Just mention that you listened to this episode, because I do get a lot of cold connection requests on LinkedIn, and I always think it’s nice if people include a short message of why they’re asking to connect. 

Kirill Eremenko: I sent you one just now. I forgot to include a message. Is that all right? 

Emily Robinson: Yes, but you I know. But yeah, that will be my last general tip. So folks, if you’re trying to build your network, really I think it’s much better if you can include, if you haven’t met that person in person, to include a short note of why you’re reaching out. 

Kirill Eremenko: Got you, and come to DataScienceGO and meet Emily in person, and then she’ll tell you more tips. 

Emily Robinson: Yeah, absolutely, thanks again. 

Kirill Eremenko: All right, thanks, Emily. 

Kirill Eremenko: So there you have it, everybody. Thank you so you much for being here today, for spending this hour with us. I hope you enjoyed this conversation we had with Emily. My personal favorite part was the topic of negotiation. I’m not good with negotiations at all. I’m learning myself, and I started diving into this area when I was at a Tony Robbins event in, I think, July last year. Hadelin and I went, and there was a workshop on negotiation. And it turns out it’s so simple, and yet it can yield such powerful results for your career, for your business, for your life. And I always love talking about this topic, so it was great to hear some advice and practical tips. I hope you jotted those down or even in your memory got something to keep from this episode that you might apply in your next interview. And hopefully that’ll give you a boost. Send Emily a note if that does help you out. I’m sure she’ll be glad to hear. And as always, you can get the show notes for this episode at SuperDataScience.com/359. That’s SuperDataScience.com/359. 

Kirill Eremenko: There we’re including the link to Emily’s LinkedIn, to her Twitter, to the book. Of course there’ll be a link to buy the book. And I highly recommend getting this book. If you enjoyed the insights from this podcast, you can just imagine how much more you will get in the book. And it’s a great way to support authors in the space of data science who are trying to make this space even easier to get into. So once again, the book is called Build a Career in Data Science. And on that note, so we’re going to wrap up. If you did enjoy this episode, and if you know somebody who’s looking to break into the space of data science, who’s looking to build a career, who is looking to enhance their career in data science or maybe change roles in data science, send them this episode, so they can also learn from Emily’s experience and Emily’s insights. And I’m sure they will be grateful. 

Kirill Eremenko: Maybe the negotiation tactics will help them out too. Very easy to share, just send them a link, SuperDataScience.com/359. And with that, my friends, thank you so much for being here today. I look forward to seeing you back here next time. Until then, happy analyzing.