From Academic to Data Scientist in Under a Year: Part II
By: Anna H. Casey
Tags: Job Search, Tech Screen, Interview, Resume, Data Science

Create a resume: Since you’re coming from academia, you are probably used to a lengthy CV. Now you’re going to need a one-page resume instead. And, I’m sorry to say, you’re likely going to have to cut a lot of important stuff to make one. Stuff like: publications you’ve authored, conferences you’ve presented at, and courses you’ve taught. Instead, you need to make skills front and center on your resume, and highlight all the coding and DS skills you’ve learned in the past few months. If you used Dataquest like I suggested in my last post, you should be able to include things like SQL, Git and version control, Bash commands, a number of different stats and machine learning algorithms, and an array of Python libraries. Be comprehensive here, because a lot of organizations use resume scanners that are just looking to see if you have the requisite skills.

When crafting your resume, do NOT lie! If you can’t point to more than one whole project in which you used a specific skill throughout, you have not learned that skill well enough to include it. For your employment history, be general (keep in mind that your employers are not coming from your specific area of academia), and highlight anything that involved coding, data analysis, data visualization, or teamwork. Make sure you include a link to your Github portfolio and LinkedIn with your contact info.

Update your LinkedIn: Is that CV of yours updated once a year on a website run by your school that no one visits? Well, that’s not going to work for job hunting outside of academia. Get a LinkedIn account, and fill in every bit of it.

Search and network: I also used LinkedIn for the job search, but any jobs website should work. Don’t limit yourself only to listed jobs, though. If there’s a company you love or a person you really want to work with, reach out, even if they don’t have any open positions listed. Ask for an informational interview and make it clear that your ultimate goal is to work there and that you’d love to know what you can do to make yourself a strong candidate if a position were to open up. Sometimes these conversations lead to jobs, but even if they don’t, the advice and networking is useful in and of itself.

While you’re networking, reach out to people you know in tech. Let them know you’re looking for a data scientist position, and ask them if they know any recruiters or DS managers. One of my interviews came from a recruiter who was introduced to me through an old high-school friend.

Contact the hiring manager: I have never, in my entire life, received a job without contacting the hiring manager when I applied. In fact, I recently found out that I never would have received an interview at the organization I ended up with if I had not sent a direct message to the hiring manager. Do not assume your resume and cover letter will get to them. Go through the online application process, yes, but then follow it up by finding the hiring manager on LinkedIn or the organization’s website. Send them a researched, personalized email letting them know you’ve applied and bringing their attention to any projects in your DS portfolio that are in their wheelhouse. If you can drop in some specifics about why you want to work with them at their organization, all the better.

Prepare for your first interview: Before you interview, find a few friends to practice your career story on. Your career story is the first question you will be asked in any preliminary interview. “Walk me through your resume,” is often how it’s phrased. They want the story — meaning they want to know why you ended up here, not just how. Why did you want to become a data scientist? Why are you leaving academia (don’t be too negative here)? Why do you want to work with them as the next chapter in your story? As an example, my career story went something like this: “I did my dissertation on a specific finding in learning theory. While I found that work interesting, I also found that the theory was very removed from any direct impact. When I moved on to a post-doc, I transitioned to more applied research looking at animal models for Alzheimer’s, but I still found that I wasn’t seeing the direct impacts of my work in a fulfilling way. I also realized that my favorite parts of my job were working with the data and coding. When my post-doc ended, I decided to pursue data science. I’ve spent the last year teaching it to myself and frankly, loving it. I’m very happy to be making the transition to something I enjoy so much, and excited to work at an organization like yours where I can use my skills to create concrete value.” Note that I did not go into detail about my academic research, and I emphasized the reasons I wanted to be a data scientist.

The other step in preparing for your first interview is to do your research. Look up the hiring manager on LinkedIn and try to find some points of common interest. Google the organization, and look at not just their company website, but other articles and websites that refer to them. Read the job posting carefully, and look for projects in your portfolio, and hard and soft skills you can highlight that match the job posting. I was lucky in that the hiring manager for the role I ended up taking had a big LinkedIn presence. He gave an interview in which he talked about what he valued in an employee, and this was immensely useful to me when I had my first interview with him. In fact, I ended up respectfully quoting his video to him when making my case for why he should give me a chance. When I had my second interview with the CEO, I watched a talk he gave explaining the organization’s mission, and realized we had a mutual hero that I brought up during the interview. Doing the research will not only make you more comfortable, but will set you apart from other candidates.

Be honest: Of all the advice I give in this article, this is my most important, but also counterintuitive advice: DS hiring managers love to hear that you don’t know how to do something. You heard correctly. In all four of my first-round interviews, I, at some point, told the hiring manager I did not know how to do an essential skill for the job. In fact, for the job I ended up taking, I was told it was exclusively R, and I didn’t know R. But I was honest with the hiring manager, and he actually wrote me an email after our interview to thank me for my honesty. The truth is that nobody expects an entry-level applicant to know how to do everything. But everyone wants an applicant who knows how to be honest with themselves and others about their skills. Emphasize that you’re a quick learner, then back that statement up by educating yourself as much as possible on the skill before your next interview or tech screen.

Know your value: Don’t be afraid to advocate for yourself. The interview process is a delicate dance, and it’s critical to remain likeable throughout, but that doesn’t mean you can’t sell yourself. In my interview for the dream job, the hiring manager started to end the conversation by saying something like “Well, it’s a very competitive role, and we’ve had a lot of applicants. I’m concerned about your lack of experience and knowledge of R.” Uh oh. I could sense where this was heading. I jumped in, “I understand. Allow me just a moment to sell myself to you, though.” I then proceeded to make the most convincing case I could for him moving me to the tech screen, and it worked. Be as honest about your strengths as you were about your shortcomings. If you get the sense that the hiring manager is thinking of going in a different direction, make the case for why they should go with you. Keep it light and try not to get sensitive. Know when to stop. But know your value and convey it. You’ve survived academia! You are self-motivated, curious, a quick learner, a big-picture thinker and a hard worker. Those aren’t skills you can write on a resume, but they are ones you can bring up in an interview, and (perhaps most importantly) they are some of the skills most prized by hiring managers.

Ask questions: It reflects poorly on you if you’re not as interested in learning if this job is a good fit as they are. What does success look like in this role? What is the culture? What’s their management style? What will be your day-to-day tasks? What soft skills do they most require? What will the company look like in 5 years? 10? If you can, try to get an idea of what the tech screen will require of you so you can prepare a bit in the meantime. Do not ask about salary, vacation, working from home, or benefits. I know it can be frustrating if they don’t bring them up (especially the salary), but those questions are best saved for when you get an offer.

Prepare for your second (and third and fourth and…) interview: Most organizations do at least two if not more rounds of interviews. There’s the first one with the hiring manager, then maybe one with the CEO or an upper-level manager, then one with the whole team. Most of the same advice applies. However, usually the purpose of these interviews is to assess how you’ll fit on the team, so your focus should be on likeability more than selling yourself.

Be sure to follow up every interview with a thank you note to every interviewer. You can use this as an opportunity to remind them of something you connected over and to highlight anything you forgot to mention during the interview. Sincere words will connect better than a generic thank you. Plus, it’s just polite.

Impress them on the tech screen: One of the first hints you’ll get about how respectful of your time an organization will be is the tech screen. Some organizations will send you a five-day project and expect you to present it in two days, and some will tell you what the project is and ask you how long you think you’ll need on it, emphasizing that they don’t want it to take over your life. Obviously, it is preferable to work for organizations in the second category. However, while the information is useful for assessing how they will treat you, you should ignore their advice and, in fact, let the project take over your life. Coming from academia, your hard coding and data science skills are going to be the biggest concerns employers have. You’re competing against people who have advanced degrees in computer science or years of relevant experience. Impressing on the tech screen is going to be critical for landing the job.

So, how do you impress on the tech screen? I think it boils down to three key bits of advice. The first is to do more than you were asked. They’re likely going to have you build an ML model. Build two and compare them. If you’re allowed, add in outside data that you think might be relevant. Make extra plots. Just generally go the extra mile. It should go without saying that the bare minimum is readable and well-documented code.

The second way to impress on the tech screen is to bring value. Find at least one insight in the data that is surprising or useful (preferably both) to them as an organization. Generate a hypothesis and find some way to back it up using the data (this might come naturally to some academics). For example, in my tech screen project for a media company with a lot of comedy holdings, I built a model that would predict comedy fans from the data. In my project for the non-profit, the task was to build an ML model that could predict food deserts. I added in a lot of extra, related data and was able to deduce that time is almost as much of a limiting factor as money in accessing healthy food.

Finally, don’t do anything you couldn’t explain to a child. The job applicant pool is full of people who learned Python and know how to use Sci-Kit Learn libraries, but don’t actually understand what they’re doing with them. In my experience, most employers are less concerned with your ability to build a perfectly predictive ML model, and more concerned with your ability to explain that model to others. Before my presentation to the non-profit, I practiced with my family, who have varying levels of data comfort. I made sure I could answer any question they had. Fortunately, if you used Dataquest, you had to learn the inner workings of a lot of the more popular algorithms. Brush up on your knowledge of whatever algorithm you choose, and be prepared to explain how it works, why you chose it, and what its shortcomings are to a person who doesn’t have a background in data science. If you want my two cents, random forest algorithms are almost always the way to go. They’re intuitive, they’re powerful, and they aren’t a black box, meaning you can examine things like feature importances.

Negotiate: When you do get a job offer, please don’t leave money sitting on the table. If you’ve just spent a couple of years on a post-doc’s salary, it might seem like the first offer they make you is a pretty good one. However, organizations almost always have some wiggle room in their offers, and they will start you on the bottom end of that wiggle room, because they expect you to negotiate. You’re coming from academia, so you most likely have an advanced degree. It’s easy to leverage education level in your negotiations. Likewise, emphasize any experience that’s relevant. As I said to the hiring manager during our negotiations, “I may not have had the title of Data Scientist, but I have had the title of Scientist for ten years now, and that means I’ve been doing data analysis and visualization for a decade.”

Tl;dr: Utilize your network, be honest with hiring managers, know your value, and go the extra mile on the tech screen. If you’ve survived academia and taught yourself data science, then landing a data science job is completely within your reach. Good luck!