Kevin Coyle, Author at General Assembly Blog

How to Get a Job in Data Science Fast

By

You want to get a data science job fast. Obviously, no one wants one to get a job slowly. But the time it takes to find a job is relative to you and your situation. When I was seeking my first data science job, I had normal just Kevin bills and things to budget for, plus a growing family who was hoping I’d get a job fast. This was different from some of my classmates, while others had their own versions of why they needed a job fast, too. I believe that when writing a how-to guide on getting a data science job quickly, we should really acknowledge that we’re talking about getting you, the reader, a job faster. Throughout this article, we’ll discuss how to get a job as a data scientist faster than you might otherwise, all things considered.

Getting a job faster is not an easy task in any industry, and getting a job faster as a data scientist has additional encumbrances. Some jobs, extremely well-paying jobs, require a nebulous skill set that most adults could acquire after several years in the professional working world. Data science is not one of those jobs. For all the talk about what a data scientist actually does, there’s a definite understanding that the set of skills necessary to successfully execute any version of the job are markedly technical, a bit esoteric, and specialized. This has pros and cons, which we’ll discuss. The community of people who aspire to join this field, as well as people already in the field, is fairly narrow which also has pros and cons.

Throughout this article, we’ll cover two main ways to speed up the time it takes to get a data science job: becoming aware of the wealth of opportunities, and increasing the likelihood that you could be considered employable.

Becoming Aware of the Wealth of Opportunities

Data science is a growing, in-demand field. See for yourself in Camm, Bowers, and Davenport’s article, “The Recession’s Impact on Analytics and Data Science” and “Why data scientist is the most promising job of 2019” by Alison DeNisco Rayome. It’s no secret however that these reports often only consider formal data science job board posts. You may have heard or already know that there exists a hidden job market. It stands to reason that if this hidden job market exists, there may also be a number of companies who have not identified their need for a data scientist yet, but likely need some portion of data science work. Here’s your action plan, assuming you already have the requisite skills to be a data scientist:

1. Find a company local to your region. This is easier if you know someone at that company, but if you don’t know anyone, just think through the industries that you’d like to build a career in. Search for several companies in those fields and consider a list of problems that might be faced by that organization, or even those industries at large.

2. Do some data work. Try to keep the scope of the project limited to something you could accomplish in one to two weekends. The idea here is not to create a thesis on some topic, but rather to add to your list of projects you can comfortably talk about in a future interview. This also does not have to be groundbreaking, bleeding edge work. Planning, setting up, and executing a hypothesis test for a company who is considering two discount rates for an upcoming sale will give you a ton more fodder for interviews over a half-baked computer vision model with no clear deliverable or impact on a business.

3. You have now done data science work. If you didn’t charge money for your services on the first run, shame on you. Charge more next time.

4. Repeat this process. The nice thing about these mini projects is that you can queue up your next potential projects while you execute the work for your current project at the same time.

Alternatively, you could consider jobs that are what I call the “yeah but there’s this thing…” type jobs. For example, let’s say you’re setting up a database for a non-profit and really that’s all they need. The thing is… it’s really your friend’s non-profit, all they need is their website to log some info into a database, and they can’t pay you. Of course you should not do things that compromise your morals or leave you feeling as though you’ve lowered your self worth in any way. Of course you’d help out your friend. Of course you would love some experience setting up a database, even if you don’t get to play with big data. Does that mean that you need to explain all of those in your next job interview? Of course not! Take the job and continue to interview for others. Do work as a data engineer. Almost everyone’s jobs have a “yeah but” element to them; it’s about whether the role will help increase your likelihood of being considered employable in the future.

Increasing the Likelihood That You Could Be Considered Employable

Thought experiment: a CTO comes to you with a vague list of Python libraries, deep learning frameworks, and several models which seem relevant to some problems your company is facing and tasks you with finding someone who can help solve those issues. Who would you turn to if you had to pick a partner in this scenario? I’ll give you a hint — you picked the person who satisfied three, maybe four criteria on what you and that team are capable of.

Recruiting in the real world is no different. Recruiters are mitigating their risk of hiring someone that won’t be able to perform the duties of the position. The way they execute is by figuring out the skills (usually indicated by demonstrated use of a particular library) necessary for the position, then finding the person who seems like they can execute on the highest number of the listed skills. In other words, a recruiter is looking to check a lot of boxes that limit the risk of you as a candidate. As a candidate, the mindset shift you need to come to terms with is that they want and need to hire someone. The recruiter is trying to find the lowest risk person, because the CTO likely has some sort of bearing on that recruiter’s position. You need to basically become the least risky hire, which makes you the best hire, amongst a pool of candidates.

There are several ways to check these boxes if you’re the recruiter. The first is obvious: find out where a group of people who successfully complete the functions of the job were trained, and then hire them. In data science, we see many candidates with training from a bootcamp, a master’s program, or PhDs. Does that mean that you need these degrees to successfully perform the function of the job? I’d argue no — it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending General Assembly is a fantastic way to show that you have acquired the relevant skills for the job.

Instead of having your resume alone speak to your skill, you can have someone in your network speak to your skills. Building a community of people who recognize your value in the field is incredibly powerful. While joining other pre-built networks is great, and opens doors to new opportunities, I’ve personally found that the communities I co-created are the strongest for me when it comes to finding a job as a data scientist. These have taken two forms: natural communities (making friends), and curated communities. Natural communities are your coworkers, friends, and fellow classmates. They become your community who can eventually speak up and advocate for you when you’re checking off those boxes. Curated communities might be a Meetup group that gathers once a month to talk about machine learning, or an email newsletter of interesting papers on Arxiv, or a Slack group you start with former classmates and data scientists you meet in the industry. In my opinion, the channel matters less, as long as your community is in a similar space as you.

Once you have the community, you can rely on them to pass things your way and you can do the same. Another benefit of General Assembly is its focus on turning thinkers into a community of creators. It’s almost guaranteed that someone in your cohort, or at a workshop or event has a similar interest as you. I’ve made contacts that passed alongside gig opportunities, and I’ve met my cofounder inside the walls of General Assembly! It’s all there, just waiting for you to act.

Regardless of what your job hunt looks like, it’s important to remember that it’s your job hunt. You might be looking for a side gig to last while you live nomadically, a job that’s a stepping stone, or a new career as a data scientist. You might approach the job hunt with a six-pack of post-graduate degrees; you might be switching from a dead end role or industry, or you might be trying out a machine learning bootcamp after finishing your PhD. Regardless of your unique situation, you’ll get a job in data science fast as long as you acknowledge where you’re currently at, and work ridiculously hard to move forward.

Explore Data Workshops

3 Tips for Preparing for a Data Science Interview

By

Hello intrepid data scientist! First off, I’d like to congratulate you; you’re likely reading this post because you’re preparing to interview for a data science job. This means I’ll assume that: (a) you’re the type of person that researches ways to improve and level up in your career, and (b) you’re reached the interview stage — congrats!

As a data science instructor, I’m often asked for advice on how to prepare for a data science interview. In response, I usually bring up three major themes. You need to:

1. Have a background that includes sufficient knowledge of the field of data science to fulfill the job’s tasks.

2. Have implemented that knowledge in some way that the community recognizes.

3. Be able to convince your interviewer of your knowledge and abilities.

1. Knowledge of Data Science

I’ve taken part in interviewing many data scientists and have also been interviewed. Through being on both sides of the table, I’ve seen that there are usually three-ish areas of knowledge that an interviewer is looking for: prerequisite knowledge of data science at large, which includes: mathematics[1], coding[2], databases[3], and the ability to communicate findings and insights[4]; knowledge of the company and its vertical; and knowledge of the tech stack of that company.

If you’re reading this article with a fairly long time horizon and not trying to cram, then you can prepare ahead of time with the knowledge of data science at large by taking a look at this blog post which has a long list of curated resources. If you are reading this and trying to prepare for a data science interview on a short time horizon, this article and this article have a list of questions with answers to get you in the zone.

Knowledge of the company is going to come from research of that company. Read up on the company and if you have time, find second and third degree connections through LinkedIn or people you know and reach out. As a General Assembly alum, I’ve found it incredibly helpful to go to a company’s LinkedIn page, check out who the fellow alumni are, and connect through a LinkedIn message or offering to buy them coffee. Reading up on the company usually takes the form of doing research about the company itself (founding principles, place in the market, investment stage, etc.), but it also takes the form of looking up who you’d be working alongside if you started working there. What does the data team look like? Are there data engineers or other data scientists?[5]

During a data science interview, your background will likely speak to your knowledge of the vertical you’re applying to. In the absence of that, some portfolio projects are a great second option to show your domain expertise.

Thomas Hughes, Manager of Data Science and Machine Learning at Etsy, shared this bit of advice on striking a balance between generalized skills, specific skills, and knowledge in a vertical:

“Companies who do not have much experience in data work generally look for candidates who specialize in their industry vertical. Since they don’t know what they’re looking for, they often will say, ‘I’m looking for someone who has solved problems similar to my problems, which I’m assuming means they have to be coming from my industry.’

More mature companies, with experience in the data space, recognize that many of the techniques are applicable across industries and don’t require industry specific knowledge, and furthermore, someone who’s deeply trained in a specific technique often adds more value than someone who’s just familiar with an industry vertical.”

Theodore Villacorta, Executive Director of Analytics at Warner Brothers, shared with me that, “regarding vertical, your background matters less; it’s more about skills to get data from a database and how you can perform with it.”

Lastly, you need to be fairly well versed in the tech stack that the company primarily uses. Villacorta offers: “Since knowledge of one of the two main open source languages is a strong requisite, along with the ability to use the corresponding SQL packages for those languages, it might be a great idea to showcase those in a portfolio piece. Most organizations have some form of SQL database.” At minimum, be prepared to answer questions about any tech stack that the company uses within the realm of data science and especially be prepared to answer questions about any tech that your resume lists. I usually like to do two things in preparation, to get an idea of what’s being used: first, I’ll head to stackshare.io and see if the company is listed. Second, I’ll look at the skills that current employees list on LinkedIn.

2. Community Recognition

The second piece is the community piece, especially if you have plenty of time before the data science interview. Community is purposefully a fairly amorphous term here. You can attend in-person events like meetups or conferences, or you can also have a community of coworkers, or a community of social media followers. I suggest laying the groundwork naturally. Networking can feel uncomfortable, but finding people you genuinely like being around in this field is usually pretty easy (didn’t anyone tell you that data scientists are the coolest people in any room?). If you don’t find a community that you’re into, try building one: set up a talk featuring other data scientists. Think like a starfish here, not a spider. You’re trying to create interactions and connections that continue to build new interactions in your absence; not interactions and connections that fall into a void once you’re no longer making them happen.

3. Convince Your Interviewer

In your data science interview, you need to convince the interviewer of your capabilities of both areas above. Interviewers are looking to make sure that you’re someone that generally fits into the puzzle board of other employees that make up the company culture. Show them that you’re great at the community thing through past coworkers or your involvement in open source projects online, engagements with people on Twitter, your writing style on blog posts, and the like. As Villacorta mentions, “For everyone, regardless of how cross functional of a role, I think it’s important to find someone who has an ability to collaborate, share resources…I’ll usually ask behavioral questions like ‘tell me a time when…’ in order to get a sense of a candidate’s abilities in this area.”

Hughes explains, “Senior level positions generally need to be providing leadership and influence over non-technical stakeholders. So they need experience explaining how the work they and their team is doing is valuable in non-technical ways.” Demonstrating your knowledge in an interview comes down to staying open. You’ve done the studying, now just get out of your own way.

I like employing the beginner’s mind here. Take every question in as though you’re uncovering the answer alongside the interviewer. In other words, think of it kind of like an archeological dig, rather than a tennis match. When you get an interview question like, “what’s a P value?” you can respond with, “are you curious about calculating and interpreting P values in the context of hypothesis testing in a project? Because I had a great project I worked on [insert teaser to a project here]… or are you looking for a definition?” This gives your interviewer a ton more fodder to work with and opens you up to answer questions in the Situation, Task, Action, Results (STAR) format, especially as it relates to former projects and jobs.

Regardless of where you are in the interviewing process, know that there is a position and great fit for a company for you somewhere. I think it’s helpful to consider the process of interviewing through the lens of a company — they’ve been looking for you! Don’t let your own ego get in the way of letting a genuine interaction take place during the data science interview. Interviews aren’t something you’re “stuck with” having to put up with on your march towards another job. In fact, they can be incredibly rewarding moments to find new areas to learn about in this fascinating field we’re in. Good luck, and let me know how it went!

Learn Data Science Online

[1] Stats questions are incredibly popular fodder for data science interviews. Linear Algebra is less often questioned in interviews, but more helpful on the job.

[2] You should be fluent in at least one of the two major open source languages: Python or R.

[3] Data lives in databases, unless it lives in dozens of Excel files on a Shared Drive. You don’t want to work at places without a database though.

[4] This is actually really difficult to gauge in an interview because everyone gives candidates leeway for being nervous. Often you can pass this test by being affable and confident in your answer.  

[5] Note that if the answer to either of these questions is “no”, then you’re going to be playing both roles.