Building a career in Data Science

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Welcome to another episode of Practical AI. This is Daniel Whitenack. I'm a data scientist with SIL International and I'm joined as always by my co host Chris Benson, who is a principal AI strategist at Lockheed Martin. How are you doing, Chris?

I am doing very well today. How's it going, Daniel?

It's going well, I think. We both have less travel on our calendars than we expected this month. Yeah, for obvious reasons, I guess. If you're listening to this episode later on, this is coronavirus season. So yeah, much of our of our travels got canceled, at least on my end. Is it the same on yours?

My March and April I was going to be traveling like nonstop all over the US in different places and it pretty much the whole smash has gotten canceled. So everybody is working remotely these days. Yeah.

And that means we have extra time to dig into great topics on practical AI and make sure and we get some good content out. So that's exciting. As part of that, today we have an amazing guest with us. We have Emily Robinson, who is senior data scientist at Warby Parker. Welcome, Emily.

Hi. Thanks so much for having me.

Yeah, and we're having Emily on the show today. She's coming out with a book with her co author Jacqueline Nolis about building a career in data science called Build a Career in Data Science from Manning. And we're going to dig into those topics here in a second, but before we do that, could you just give us a little bit of information about your background and how you got into data science?

Emily Absolutely. So I was lucky enough when I went to college at Rice University to be there when Hadley Wickham was a professor. And for those of your listeners who might be not that familiar with R, do more in Python. Hadley Wickham is one of the most well known R programmers. He's created some of the most popular packages especially focused on data analysis. And so I started learning R in university from classes he had designed. My major was when I created myself called Decision Sciences, which was focused in the social sciences with a minor in stats. And then I went on and got a master's degree in organizational behavior that was actually part of a PhD program. And I decided two years in that academia wasn't necessarily for me. So I off tracked. I got my master's degree and then I came back to New York where I'm from, and did a data science boot camp medicine. The reason I was drawn to data science is the process I found was quite similar to the social science research process. You would come up with a question that you want to investigate, find the data, analyze it, and then present it to people, whether that's an academia, someone very much in your niche, or sometimes to a professor in a totally different department. And what attracted me to data science was being able to do that for companies, making an impact and on a bit of a shorter time scale than the seven years it can sometimes take to publish a paper. And after that boot camp I went on to work at Etsy and then at Data Camp for data Science for both of those companies and specializing in a B testing or online experimentation. And now I'm here at Warby Parker. I started back in December, so I've been here a little over three months now.

Oh, congratulations.

Yeah, thank you.

That's great. Yeah, what a great opportunity to get that early start in R like you did. And I know you're still pretty involved in the R community, aren't you?

Yeah, I really love the R community. Metis. The bootcamp I did was in Python and I think it really did help me to learn Python and also more machine learning in that bootcamp. But I've actually been using R since I started working in data science and a big part of what attracted me to it and has kept me in it is the community, which I find an especially friendly and welcoming community and especially towards people who might not consider themselves Programmers and are using programming as more of a one part of their toolbox for, and are more focused on the ends of what they're trying to program than necessarily being like, I need to make the most beautiful code. And yes, I've been involved in the art community, especially with Our Ladies, which is a global organization to encourage gender minorities programming in R. And also on the R community on Twitter, which is again, just a very active, welcoming. You go to Twitter, you ask for R help, and immediately you have all different people coming in and helping you figure out what your problem is.

Yeah, I can definitely. So I work most of the time in Python, but I have done some things in R and I was really happy to attend the R conference in New York. I think it was two or three years ago, something like that, that Jared Lander helps put on. And that was a great experience. And I felt, you know, I was a little bit nervous because I felt a little bit like an outsider or a poser because I wasn't, you know, didn't have extensive background in R. But it was a great experience and the community was so welcoming. So I can definitely attest to that. It's a really great community.

So I'm kind of curious. You know, you kind of took us through the, you know, working for several organizations here, and you've gone and written the book Build a Career in Data Science. So what was it that made you want to write this book on Building a Career in data science? And how did that come about? And how did you get connected with Jacqueline Nolis?

Yeah, and I think this is a great example of, you know, you don't know what's necessarily going to make a huge difference in your life and your career. Jaclyn and I met back at Day To Day Texas, which is a conference in Austin in January 2018. And it was sort of interesting because that conference had mostly been like a graphing conference, but that year they decided they wanted to do an art track. And so they had my brother, David Robinson, who is also an R programmer, Hilary Parker, and Jacqueline and me, among others, speaking. And that's where Jacqueline and I first met. We attended each other's talks. And then a couple months later, Jacqueline reached out to me because Manning had gotten in touch with her, asking if she was interested in writing a book. And Jacqueline reached out to me and said, you know, I know you've done some of this writing in this, like, career advice space. Would you be interested in writing this book with me? And that was another example of when I was writing so I'd previously written some blog posts, including some like more Career Focus, which I think partly came from my background in organization behavior, that I'd studied some of these topics. And, you know, I think that helped Jacqueline beyond meeting me. You know, she was. She had read these pieces, she felt I was a good writer, I had something to say, and that's how we got started writing this book. And I think a big motivation for me has always been really trying to scale up advice. So I do meet with people one on one. I write a blog. But this book felt a really good way to dedicate a lot of time to thinking about these topics, to learn from. Jacqueline, who comes from a bit of a different background. She's been in data science longer. She has a PhD industrial engineering. She's been a data science consultant, a manager, and so on. So having her input. And then also at the end of every chapter, we interview a different data scientist. So we have people who have bachelor's, we have people with PhDs, we have folks who are very heavy in machine learning. We have folks very much on the analytics side of data science. And again, like, you know, maybe we could have done a blog post series or something like that, but having a book really gave us the ability to dedicate a lot of time to putting a resource out in the world that we wish we had had when we were getting started in data science.

Yeah. And in that process, I mean, you mentioned that Jacqueline's background was kind of different and you interviewed a lot of people. Did your perception of people's track through a data science career and how data science careers are happening these days, did that shift through the process or were you surprised by certain things?

Yeah. So I think one thing that's interesting, this will be interesting how this will play out in a few years, because I think Vicki Boykus, we had her write a little blurb in the first chapter called Data Science is Different now, based on a blog post she wrote by the same name, where basically she's saying it's getting harder to enter data science with boot camps and new master's degree programs. You have a lot of people entering the field, and it can be really hard to differentiate yourself. So I do think, you know, most people in who we interviewed have been in data science, like at least a year or two years because we wanted people who'd had some experience. But I do think it will be interesting a few years to now to look, okay, folks who are entering at the moment who are looking for the first data science job, I do think it's the landscape is changing some. I think a lot of the principles do remain the same in terms of like networking is going to be really important, writing a resume. Also half of our book is once you have the data science job, you know, how do you do well in it? I think people we talked to, we didn't come in with very strong expectations because we already knew it was such a diverse field in terms of backgrounds and interests and career paths. But I think that I'm interested to see more how it will keep changing in terms of it was we would see with some of our interviewees it was very possible five or ten years ago. You know, everyone say like oh, I don't necessarily have a typical background, but there wasn't a typical background, there wasn't a data science degree 10 years ago. So everyone was coming in with different stuff. And how that will change in five or ten years from now when people can major in data science. And are we going to see it's harder for example for people from the social sciences to enter.

So you know, and that raises, it raises an interesting point when you talk about people coming in from different places. What have you found about building a career in data science that is, that's different from other technical careers? You know, whether it be software development or maybe the other sciences. What did you discover along the way that was distinct about building a data science career from other areas?

Yeah, I think one thing that is distinct is that there's not necessarily like well trodden path and that it's not as well the field is not as well defined. And that can mean for example like interviews at company can just totally run the gamut. So for computer science folks, for example, you're trying to get into a software engineering job, there's a book cracking the coding interview. There's tons of resources out there like what questions did Google ask or Facebook. If you look at bigger companies and even smaller companies have kind of adapted this. But in data science right now you might have one company that doesn't give you any coding in your interview and another one has you whiteboard code and another one has a take home project you do in Python and another one asks you to derive something mathematical while you're in the interview and another one has you do consulting type problems. And I think as a field that can make it really challenging to enter because that's coming from data science is such a broad field and there's so many different parts to it and it can mean that it's very easy to feel Imposter syndrome. Because, you know, how are you ever going to know it? And the answer is, well, no one knows it. And because it's all these, like, separate overlapping fields that have, you know, are very deep in their own right, I think it can be quite intimidating, especially when you come up with lists of like, you know, true data scientists need to know these 10 algorithms and, you know, be like, know how to deploy things in the cloud and be an expert at managing stakeholder relationships and have a degree in math.

Easy stuff.

Yeah, easy stuff. Exactly. And I do think one thing that does give me hope, though, is I do see data science similar to how computer science went. Where, for example, you used to have like a webmaster and that doesn't exist anymore, Right? No one's a webmaster. Like, things have started specialized and it's not because, like, oh, no one works in software engineering or runs websites anymore. Of course not. But it's become more specialized. And I do see in data science they're starting to become things like Airbnb has all their data science job postings always are one of inference analytics or machine learning. And I think obviously there's still some subgroups within there, but I think that's a very good start to helping people realize that these are distinct jobs. And someone who is a very good fit for a machine learning job there, for example, probably is not a good fit for an inference job because those do use different skill sets within data science.

That's an interesting point that you raise and also you're going back to a moment to when you brought up imposter syndrome. We're at these early stages of this field and all of us are coming in, whether you're coming in soon after college or university, or whether you're transitioning from another field and you, you've been in a career for a while. That lack of standardization, I think affects everybody coming into the field to some degree. Did you find any similarities or differences for people entering this field based on those different points of origin from which they entered?

Yeah, I mean, I do think, you know, I've been asked before, like, how do I like for folks who are developing this understanding of, you know, what data science looks like at different companies is very different and specialties, you know, how do I figure out what I want to do? I do think there's some too. If we think of the Airbnb, which we also have a similar breakdown in our book of what we see as the different areas of data science, analytics, inference and machine learning, machine learning, often people Especially for production machine learning, putting a recommendation algorithm on a website. Often those people come from engineering backgrounds and inference, it's often statistics or quantitative social science. Analytics might be, you know, some of both of those. But also you're often more directly dealing with business stakeholders. So maybe you used to be a consultant or you have domain expertise, you're doing analytics for marketing and used to be in marketing. So I do think there's some of, you know, depending on your background that lead it easier into one of these than the others. But on the other hand I have seen people change. Someone I worked with at Etsy who was more on the analytics side, what he ended up doing was he you could at Etsy you could like boot camp with another team for a month and sort of shadow them and help them out learning. So in his case he boot camp with some soft with a software engineering team. So he learned more about software engineering, he contributed his analytics skill and he used that to help transition into a more production machine learning data scientist role. So that was an interesting case of where he started out doing one thing and then shifted within data science to a different type of role.

Hi there, this is Daniel Whitenack, one of the co hosts of Practical AI and when I'm not working on practical AI I'm developing my own AI applications or I'm training teams at other companies. I've been doing this for over 10 years now and I've trained more than a thousand people. Now I'd like to invite you to my new live online training event called AI Classroom. In AI Classroom I'm going to teach the practical skills I've learned over the years using the latest open source AI technology. You will learn both AI theory along with practical hands on implementations in Both PyTorch and TensorFlow. After attending AI Classroom you'll be able to understand the latest models, implement your own models and code train computer vision and NLP models, create model inference servers and experiment with state of the art methods like reinforcement learning. AI Classroom is taking place this May. It'll be taking place live and completely online in a high quality virtual classroom so no travel is required. There will be two cohorts with convenient time zones for eastern and western hemispheres so don't miss out. Tickets and more information is available at Datadan IO. That's Datadan IO and early bird pricing lasts until April 3rd. See you online in AI Classroom. So recently we had a show, actually I think our last show, which really talked a bit more about options around data science education but we didn't get a lot into the sort of day to day of being a data scientist. And I know that you've already highlighted that that can look very different at different companies and the positions can be very different. And one of the things that I thought was pretty interesting about your book was that you highlight some sort of what some typical companies are like to work at each day. I was wondering if you could maybe share a couple of your favorite examples of those types of companies in terms of the profile and how they're different.

Yeah, the biggest part, we do this and in chapter two, which is like what that whole chapter is about. And that was a fun. So Jacqueline and I split writing the chapters and so each wrote half and that was Jacqueline's chapter. And that was really fun for me to. But we reviewed each other's chapter, of course, and gave feedback and that was fun for me to read because that was one of the chapters that I felt like there's not that much material out there on it. So you might have a blog post if someone says, here's my experience working at startups, but they don't necessarily have experience at a big company or government contractor. And so it's not really contrasting it. You just get a window into one type. But yeah, so one fun example, I think there is comparing, for example, talk about in chapter nine, onboarding between a small startup and, say, massive tech company. So if you're working at a small startup, especially if it's a very small startup, it's like maybe they have the laptop for you that day, maybe they don't. You know, you're probably not going to have any systems set up. You might have to try to figure out how you even plug into the data source. The data source may have been set up to help, you know, push data to the website and not for you to analyze. So the first time you try to count how many customers your business had, it could take six minutes for it to return that there's a million. When of course, if you're running SQL, it should take a millisecond, versus if you're at a massive tech company, you probably have a week full of very structured onboarding. There's tons of documentation. You might end up spending a lot of your initial time just reading about what the team has done. Trying to get a handle on the full tech stack is pretty much impossible. It's a very different situation there. And there are pros and cons to each, of course. I was talking to someone recently who came in as the first data scientist and she really deliberately did that. She had some previous experience because, you know, she felt then she was basically in control. She could make sure that from the beginning she was setting up in a way that she thought was best versus having to deal with the tech debt and in some cases things that were unchangeable now and, you know, she didn't think were the most efficient. So I do think what we lay out is less of a, like, you should definitely do this or you definitely shouldn't do this. But like at the end of chapter two, for example, is, here are some factors to think about. So maybe the company you're looking at doesn't fall into, like, one of the five example types we give. But think about what mentorship opportunities will you have there? How will the pay be? What about the autonomy? What about the learning opportunities? And that can really help you lay out and figure out, given what's important to you, what type of company should you be going to do, you think?

Because there's so much hype around data science and AI and machine learning these days, in some cases it seems like companies are trying, like, they feel like they need a data scientist or an AI person or that sort of thing because it's going to give them an edge, but they haven't really explored that, like, what opportunities they have internally for that yet. Are there ways to kind of understand what a company profile is in terms of their commitment to data science and how essential it is to part of their business and how that affects the day to day?

Yeah, absolutely. That's a great point. And actually our Chapter one interviewee, Robert Chang, shared his first experience in a data science job. So he went and worked at the Washington Post, and what he quickly realized was that they had almost no data infrastructure set up. So actually he ended up working on data engineering basically for the first year when that hadn't really been what he wanted to do. Like, as he said in an interview, he was hoping he wanted to do data visualization. And he's like, well, the New York Times has really cool data visualization stuff and the Washington Post, it's a newspaper, so let me go there. And what he said is, now he really recommends to people to ask a lot of questions in the interviews, ask about what's the data engineering team look like? Is there a data engineering team? What's, you know, how big is the data science team? How long have they been around some of this? You can, you know, find out online. You can guess. Like most people could, you know, probably realize that Google, Airbnb, Most like, you know, mature tech companies have pretty big data science teams. Whereas like a legacy company might be less likely or to have, you know, it might be a little harder for them to integrate it. But you know, once you've done your own research, Glassdoor is great. Just Even looking on LinkedIn, like, is there someone with a title data scientist who works at this company? Definitely. An interview is a really good place and I would certainly say that it's important to remember that an interview process is a two way street. You are also interviewing the company and it can feel sometimes again, especially with the hype around data science and you know, a challenging job market, that I just want to be hired as a data scientist. I don't really care where. So I would say like pay me, I guess, a decent salary, but you want a place where you can, you know, be learning and thriving. And unfortunately there are some companies that aren't, I would say like universally bad, but may not be good, for example, for someone who is new to data science and might benefit from some more structure and mentorship.

So, you know, you raise a really interesting perspective there about kind of the job market and some of the issues that people face and trying to differentiate it a bit in terms of what they're looking for. How do you distinguish between these different opportunities in the job market and you know, as part of that, you know, what does the demand look like in each of those areas and how do you prepare? I noticed that you talk about, you know, portfolios in your book and you know, so how do you use some of these tools to address each of these different parts of the job market that you might have an interest in?

Yeah, I think the job market right now is pretty good for people who have had at least like one year of data science experience. So like with a job title like data scientist or something very similar, I think it is harder for people who are say coming out of a boot camp or undergraduate or trying to just change careers, you know, doing online courses just because it's, you know, there are just more of them. Right. There are fewer people who have had experience working in data science and, and companies use different methods to understand whether someone speak good. And often the easiest one for a recruiter to do maybe doesn't know the field works that well is just did this person have the title that they would have here, which means some other company said that they could do this job or at least thought they could do this job enough to hire them and they've had experience doing it. So what do you do if you haven't had. How do you get over this paradox of needing experience? Like get experience, like needing a data science job to get a data science job. And I do think that's where the portfolio piece can really help. So a portfolio would be having your code and some projects up on GitHub and I think ideally on a blog. Why I think that's important is one to have something to show employers to show that you can do the job that they're asking you to do. That you can take a question that you come up with, find the data, analyze it and present it back. Whether that's by writing a blog post or by say making a web application. I do think this can really help yourself stand out, especially if it's a original portfolio project. I don't think what would be really helpful here is, for example, your script for trying to predict who's going to die on the Titanic, which is a very classic kaggle data beginning data problem, because that's not especially original. Like, who knows if you copy that code from someone else versus like, hey, I'm really interested in fashion. So I use New York Times API to pull all the fashion articles. And then I did some topic modeling to see how different trends came in and out of fashion. Right? Like, that's a very original idea. It shows some personality too. And I think that can be a really strong way to help you stand out. But at the end of the day, I mentioned a little bit earlier, networking is really important. A lot of jobs go to someone who was referred by someone who's currently working at that company or through meeting the person who is hiring or meeting someone related on that team. And I think trying to expand your data science network can be a really good step in getting that first job.

So I'm curious. It seems like when I got into data science, I feel like I got kind of lucky because it was kind of the start of the hype and there was still there weren't a lot of people filling those positions. And so I kind of got in at that time and then there was like the big data science hype. And now we're kind of going through a lot of emphasis on AI and neural networks and deep learning. How is that whole wave of influence from the AI side of things shaping the data science job market? And, you know, is that putting pressure on people to say, you know, on top of all the other things listed in a data science, you know, position learn, you know, learn TensorFlow and have implemented their own, you know, implementation of this or that as well. How do you see that shaping things?

Yeah, I think absolutely that is putting pressure on people, but I think in some ways it's more pressure from peers. As in like, I think there's some companies who are getting really caught up with that. But like the companies who know what they're doing, like generalize. Most data science problems don't need AI or deep learning applied to them. And in the cases that they do, sometimes those go to people with honestly like very specific backgrounds in that. So for example, like you know, Google self driving car division. Right. I'm sure they have a ton of research scientists with PhDs in very related fields. But I do think people are putting their pressure on themselves to learn that. And actually I don't think that's what you should be focusing on because again, most of the problems you're going to be faced with in data science and certainly at the start of your career won't need that. And it's much more important to, you know, just get very. My brother Dave, actually we interview him in chapter four for the portfolio chapter and he talks like his advice is not to focus on that deep learning like TensorFlow, the cutting edge of the field stuff, but to get like very comfortable, you know, manipulating data, summarizing it, visualizing it and so on, you know, making some more basic models because that really is like day in, day out, much more what you're going to be doing along with of course things like managing stakeholders and communicating and all of the quote unquote, softer skills.

Yeah, so that kind of begs the question. You talk about Dave's recommendation and such. If you're out there and you are searching for a data science position, how do you identify the right position for you, the one that fits your desire, your need? How do you know when you've arrived at that?

Yeah, I mean, I think it's tough because there's only so much you can know before starting a job, like even if you ask questions in the interview. So actually Jacqueline and I have A post on 12 red flags in data science interviews talking about like what you should look out for in terms of what questions they ask you or their answers to your questions that we recommend you ask. But you know, given that there's still going to be unknowns, you know, maybe you end up with a really bad boss or dysfunctional department. In that case, I do think it's important to remember like you can learn even from bad jobs and to also think about how can you design, like given your situation, like in a job you Know, usually there's still some room to job craft. So I really like this book, Designing youg Life. And they just came out, and the authors just came out with a new book, Designing a Work Life, where they're talking about, you know, thinking about like what you want to do. A lot of people turn to like, let me like sit in a room and like think for a while and introspect. And they're much more on the design process which really advocates for like try things out and iterate and then reflect basically. Right. Like, don't just like go in a box like, oh, would I like this type thing? Would I? It's like, well try it and you know what exercises imagine like think about how much you'll enjoy how much you think you're gonna enjoy it. Reflect on how much you actually enjoy it. Go through your day and mark off like, you know, at half hour increments, like, how's your energy level? Then you can reflect back and you can see like, oh, wow, it looks like my energy level was high when I was, you know, collaboratively coding or in meetings and actually pretty low when I was by myself for a couple hours, which, you know, maybe I didn't expect. So I do think like once you're in a job, that's some things you can do before you get a job. I think generally what makes jobs appealing to people, there are some universal traits. One of them is having autonomy, another one is having the ability to learn. And so I think focusing on those and having, I would say a third thing, like some mentorship, some support, those would be the type of things I would look at. And you know, if you're very new to data science, be a little bit flexible. Don't necessarily say, oh, I know the only thing I want to do is like make TensorFlow models and I only want to do it at Google and I only want to do it with a data scientist title. Right. Like data science happens in a lot of positions that don't have the data scientist title. So I'd also advise people to maybe let go of that a bit because then once you broaden that scope, you might find a lot of really great jobs out there that maybe you wouldn't have found if you only wanted to have that data scientist title.

That was in fact a very narrow set of search criteria right there.

Yeah, and I think it's a good point because I mean, once you establish yourself, at least my experience has been, let's say that you do have those ambitions to, you know, to train, state of the art models and all of those things. My experience is even if there's a company that's currently doing that or exploring that, it's generally not the first thing you're going to be doing with them. So regardless, I think it's beneficial to really develop a good understanding of the business processes that are happening there, develop good relationships at the company and understand, like, what problems are important to them. Because, you know, once you have a better understanding of that, eventually, you know, maybe it is that you can, you know, proof of concept, some more advanced sort of model or something like that, but you're not going to be able to convince people that that's even worthwhile if you don't even understand the business processes and if you don't have good relationships and all of those things within your team. I was curious. You mentioned certain things that, that you had thought about in terms of things to avoid or red flags and that sort of thing. Given that there's so many posted data science and AI and machine learning positions out there, I was wondering if there are any sort of tips you had in terms of filtering through that noise. It can be really overwhelming for people because they see all these positions and, you know, they're so varied. You know, one's talking about, oh, you need like Hadoop and Spark and TensorFlow and PyTorch and reinforcement learning and whatever. And then the other one is like a totally separate set of tools. And do you have any good resources that people might utilize in terms of searching through job postings? Or maybe it's about, like you were saying, networking. Are community events a good way to deal with that sort of noise?

Yeah, I think community events is one way. He had mentioned earlier the New York conference run by Jared Lander. He also runs a monthly meetup, and at the beginning of each meetup he asked anyone who's hiring to announce that. So that's one way. I do think, you know, you can apply, like, you can add search terms, for example, rather than just like searching for data, science or analytics. You could be like analytics R. Like, say you want to do online experimentation, experimentation that might narrow down the options you have to look through. I do think, you know, there's only. I'm not sure I have any tricks besides adding those search terms, but I do think once you're reading a job description to start filtering them out. Definitely looking for a job that seems to want a unicorn that, you know, wants someone and says they're gonna be like, you're gonna be making dashboards. You're Actually doing deep learning models. And you're also gonna like run our online experimentation system and you're also gonna like do this and that and like, you know, and just this whole laundry list of things that for example, if you go back to like the analytics, inference, machine learning, like hits all three because the problem there is not just like one. It's unlikely that there's anyone who's gonna be really good at all of those. But also you're gonna be pretty overworked if you're expected to do all these very distinct tasks. So I would like, when you're glancing at descriptions, try to see, okay, does it seem like this is an actual person or are they talking about a full data science team and expecting that to be in one position. And again, that's going back to like, it is a two way street. And you can also be picky and not just like, oh, like might not, you know, might be less likely to get that job. It's like if you did get that job, it's usually not going to be a good experience. So I think that's one way to filter them out.

My little tongue in cheek warning is if they say they need 10 years of TensorFlow experience, that's a warning sign right there.

Yeah, exactly. And I do think there's, you know, this also though depends on like where, where you are, right? Like, you know, San Francisco, New York, a few other cities, you kind of have the luxury of lots of job postings to sift through, but if you're in a smaller town and you need to work remote, like, it might not be the case that you end up having that many. So I don't know if it's necessarily a problem that everyone is facing that's like there's just too many data science jobs.

So, Emily, I got a question. I want to kind of figure out where we are, you know, in job searches. We're always talking about resumes and cover letters. But I guess my question is we're in this age where, you know, everyone is on LinkedIn and we're even, you know, we've actually had episodes where we're talking to organizations that are now, you know, doing analysis of job applications with different AI models and stuff. So are the traditional resume and cover letters still relevant in this day and age? Have they changed where, what should people be thinking about now as they're, as they're looking at prepping what they need for a job search?

Yeah, absolutely. Traditional resume and cover letters is so important. Like most cover letter can vary by companies but almost always like even if you referred somewhere, you're going to have to submit a resume. And it's not necessarily the case that like, oh, I was referred, like, so I'll just like submit whatever. Like they're definitely going to interview me. You know, the critter could still say like no, but the hiring manager is still going to look at it and decide whether they want to spend half an hour talking with you. So I say absolutely. It's still very important. The other things like a LinkedIn or a blog or a GitHub, I think it really helps bring attention to your profile and maybe have companies reaching out to you or enhance your application. But you're still going to need that resume or cover letter. And I was sort of, we have a chapter in our book about this and it's sort of funny because there's a lot of advice on resumes out there. You know, some of it's field dependent but some of it's like somewhat universal. But I've been surprised at like how many resumes I've sometimes seen by people that I think would really benefit from following some advice. So I think the big ones here would be like almost always just do a one page resume. Like unless you have many years of relevant experience, fit it onto one page because it just shows like you can be concise, it's a lot easier to scan and with that easier to scan page, have some white space and don't like have it necessarily just like filled wall to wall with all the text you can cram in there in size 10. Because you want someone who's glancing at it for say 10 seconds to immediately be able to zero in on the important points. And all it needs to do is just get you in that door, get you into the hiring manager interview and from there you know it's going to be based on your interview, like how your interviews go and maybe some other pieces like your portfolio, like your blog. But you still need that resume is still a very key component and sometimes a cover letter in getting you in the door in the first place.

So let's say that you did get in the door and you're about to start ramping up in a new data science position, AI position, machine learning position, whatever the title is. What are some of the first things to focus on as you're settling in to that new position? I know you focus on this a little bit in the book as well.

Yeah, I mean this is something that varies by company of course, again, like how it looks, what you need to do in your first couple months at a startup where you're the first data scientist is quite different if you're joining, you know, mature and like well functioning team. But that being said, I think there's a couple of principles that really apply to any type of new role and a big one there is really trying to learn as much as you can and not being afraid to ask questions. That doesn't mean like necessarily you should ask a question that you could easily Google, like what is the difference between a vector and a list and R. But really don't be afraid of asking questions. Trying to understand, you know, where does something live? Where can I find docs on past projects? You know, why do we do things this way? You know, what Slack channels should I be in? What does this data mean? And so on and so forth. And to ask those really with a sense of curiosity and not a sense of, for example, not quite entitlement, but like, you know, why do we do it this way instead of this like, clearly superior way that I learned in school, right? Like, you don't want to come off immediately as like, oh wow, I can't believe like you all are idiots and I can't believe you're doing it this way. And I'm so glad I'm here to like fix it all and really try to keep an open mind. Which doesn't mean, of course, that like everything they're doing is perfect. Ideally you came in and you were hired because you have a lot to contribute, but in those first couple months, really focusing on learning the most and trying to set yourself up to be productive in the long term and not worry too much about like, oh wow, I really immediately have to start delivering or they're going to wonder why they hired me. Because I think if that was truly the case, that's usually a sign of not a very good or supportive company. If they're expecting you to like immediately start delivering reports and other things like, of course you can start doing small stuff, but they recognize it takes time to ramp up and you don't want to become too focused immediately on the short term rather than, for example, spending a day or two making sure, you know, building some internal functions. For example, if you're the first data scientist to make it a lot easier for you to load data, which is something you're going to be doing every day from now on. And by saving 10 minutes every time you do that is really going to pay back in the long term.

So I actually want to extend one of the, you know, a part of your answer right there. And that is, I've heard many people in data science jobs say that the hardest part of the job isn't about the data itself. It's really about the people you know at the organization. And you kind of alluded to that in that last answer in terms of expectations and such. And so you know, if you are going into a position and maybe that organization is not a data driven organization culturally the way that some of the leaders in the industry might be, and you're trying to work with people and show them the value of data driven methods, how do you go about developing influence with those people and being able to help them see the benefit of driving their own decisions via data rather than maybe just their own experience, their own sense of ego that hey, I've already been here and I know what I'm doing. How do you contend with that?

Yeah, that certainly can be challenging. I think there's two things that can happen. One is almost like who needs data? I have intuitions. The other is like, great, we have a data scientist, like build a model that will predict whether, which sales customers will churn, right. And then it turns out like they don't even know how many sales customers are leaving each month. They're not even quite sure what their ARR is. Like no one's even done a descriptive analysis, right. Which might turn up something like, oh hey, it turns out we have a big problem with very small customers churning and you don't need to go immediately build a fancy model. Let's get them just the numbers. And to understand actually their maybe we don't even focus on churn at all. It turns out that we've been really slowing down on acquiring new customers that's going to harm us in the long term. So sometimes it's about redirecting that. And I do think something that can help there or if they're numbers averse is starting to figure out, okay, is there a champion? So for example, at a smaller company, a startup, it's not unusual for many employees to have direct relationships with people in the C suite. So for example, maybe the sales head is like, like has some numbers but like, you know, is maybe you're finding out that like they're not doing as well as they said they were. They're not getting returns on some of their sales hires. And you know, of course they're not very motivated, maybe not that interested in hearing that. But the CEO would be right, because that's their, that's their bottom line. Maybe they're more metrics driven. So that doesn't say like, you should never talk to the salesperson and just go above them. But if you are finding sometimes, like, well, you know, like, I've been really trying, I've been working to develop a relationship, developing empathy, which is very important. Like really trying to understand the questions behind the questions, what problems they're dealing with. If you're finding that that is not being fruitful of maybe seeing, okay, do I have to go to someone else to talk to them about that or do I just like, are there other places I can add value? Because I do think it's hard. Like some, you know, how much politics are you willing to put up with, basically? Right. And you know, given you try things like have empathy, talk with people, communicate well, really try to understand their problems. At the end of the day, sometimes you could do a lot of work and it just won't necessarily get received that well. And then you do have to make a bit of a decision about what you want to do with that.

So as you're kind of settling into your position, maybe even you've had a couple of different data science positions or you're understanding more about, you know, what you want to do and what you want to learn over time. What are some good ways to continue your personal development as a data scientist through throughout your career? What are some of the things that are kind of easy wins that you can be involved with or, or integrate into your career workflow or other things?

Yeah, I think this really depends on the person. So for me, one of the things I really enjoyed doing more when I got starting at all, when I started as a data scientist was to begin speaking. I found a lot of opportunities through that. As I mentioned, this book with Jacqueline came about because we saw each other speak at a conference and I never would have met her without that. So that's one thing is, that's some people call it conference driven development. So I know some folks who give talks, for example, who are saying, like, oh, I'm going to give a talk about the package for a package they haven't created yet. And putting that deadline on themselves really helps motivate them. So another way people keep learning is doing open source. So for example, let's say you're one of the only data scientists at your company. Maybe you want to get involved in a big open source project because there you can learn more about. All right, what's it like to work with a bit of legacy code, to have many collaborators on a project, have to think about, you know, there's thousands of users, you know, we can't just be changing functions willy nilly. That could be one way. Other people like to do online classes. I will say for me, and I think most data scientists I've talked to, I would be wary of just say like doing an online class or something like that without having an application. I think most people learn best or can overestimate their learning just by like watching lectures, even doing little problem sets and learn much better when they have to take that and apply it to a project that they're working on, whether that's a personal project or one at work. So I think you've got a lot of different options. So right. Whether you want to do speaking, more blogging, you want to do personal projects, you want to say like I don't, I'm not really interested in doing stuff outside my work. Like my work is really intense. I want to just focus on projects at my company. I do think there's a lot of different ways you can try to keep learning and keep growing your skills.

So I got a question especially that I think applies to a lot of companies that may not have that long term culture of data science. And that is the idea of failure and you know, failure in applying data science. There are so many factors that can cause a data science initiative to fail or to go awry or there's not enough data. And so, you know, when one of those hits, how does the data scientist, or maybe somebody who's focusing more on the neural network, side, AI side, how do they gracefully deal with and learn from those fail projects? And as part of that, how do they communicate the normalcy of that state to stakeholders within the organization that might not otherwise have arrived at that same understanding that the data scientist has?

Yeah, so I think there's a couple of things with dealing with failures. So one of those things is you don't want to come in and surprise them. Like, oh yeah, we have been working on this for like four months and I've told you it's all going well or I haven't told you anything and like surprise, it's all failed. You don't want to shock people. And so how you can avoid that is having fairly frequent check ins where you're like, okay, here's our plan, like here's how we're progressing to that. You know, here's where we ran into an unexpected like bug, you know, here's our plan for getting around that and so on. And so that way, I mean you could be doing that and maybe it does turn out like wow, we like there was some external shock or like you know, we were going off for a couple weeks like with this part of the data set and we only just got access to this other part of the data set. It turns out it's totally useless and so we can't do the project. But at least if you're checking frugally it's less likely that will happen. And also if you do a good job upfront of explaining the risks. So there is a lot of unknowns of course but as much as you can at the beginning saying like alright, here are some parts like we don't know like house data availability, like what's the likelihood of like the, the impact or the gains we're going to get. And the final step I would say is trying to make a balance of projects because all data science projects like aren't created equally risky. So for example prediction models can be fairly risky because like it there just might not be signal like you might not be able to predict the outcome with the data you have versus a more infrastructure related problem like I don't know, setting up a preliminary a B testing tool or a more descriptive problem like all right, let's surface, you know more about our customer data. Let's build these dashboards having that can make sure that you have a little bit of a balance. So it's not for example going to turn out like wow because we took on all really risky projects like don't really have much to show for it in terms of things that will benefit the business in a whole years of work because you don't want to be in that situation.

Yeah, that makes sense. I hate to you know, end our conversation on the topic of a failed project to give a little bit of brightness at the end here. I do want to again mention your book book but also that the Manning was, was nice enough to give the podcast a really great discount code for the book and there's so much more in there. There's so much more about the you know, career path of a, of a data scientist and interviews and job offers and all sorts of things that we didn't have time to cover. The discount code is pod pod pod practical AI 19. We'll put that in the show notes as well. So make sure and utilize that and look up the book and also follow Emily and Jacqueline with a lot of the great content that they're putting out there in the, in the R community and elsewhere as well. So thank you so much for joining us and sharing your thoughts.

Emily thank you again for having me.

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