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 ¡ª 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 ¡ª it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending 足球竞彩网 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 足球竞彩网 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 足球竞彩网 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.
