Landing Your First Data Science Job
Touted as the “next frontier for innovation,” data in the business world has been exploding in recent years, and with it, the rise of data science as a profession. In fact, the U.S. Bureau of Labor Statistics predicts that by next year, the demand for data scientists will outstrip supply by 50-60%, resulting in a severe shortage of the talent that organizations need in order to take advantage of big data. McKinsey & Company predicts that the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the expertise to make effective decisions using big data.
Needless to say, there’s never been a better time to pursue a career in data science. You’ll have a plethora of career opportunities, enviable job security, and the ability to directly impact the decisions and direction of a business. What’s more, with an average salary in the US that’s up 6% year over year to just under $140k, this field is financially lucrative.
With all of that in mind, it comes as little surprise that a career as a data scientist was actually ranked as the best job in America. So how can you get a job as one? These tips will set you on the right path.
Transition from academia or from a business analyst role
The reality is that there are very few entry-level data science positions, so you’ll probably have to start in another field and sharpen your skills in a few key areas before moving into a full-time role.
While there are many paths into a career in data science, two of the most common are through academia or via a role in business analytics. Individuals with advanced degrees in fields like statistics, computer science, applied economics and the sciences who are accustomed to working with large data sets are increasingly being drawn from careers in academia into the tech industry. Airbnb has a great post about what they look for in these kinds of candidates.
Business analysts who have extensive experience with reporting, dashboards and database querying also make good data scientists because of their familiarity with making sense of large amounts of data and translating it into actionable advice for their respective companies.
Learn Bash, Python, SQL and R
No matter what industry they are in, successful data scientists possess a combination of technical, analytical, and presentation skills. Perhaps most importantly, they are creative problem solvers and have a passion for answering difficult questions. And while the technical skills required vary from one company to another, almost all aspiring data scientists would be well-advised to learn four basic coding languages, including: Bash/Command Line, Python, SQL, and R. Which of those you’ll end up using will vary based on the company you work for, however, once you’ve learned one, it’ll be much easier to pick up the others.
Enroll in coding bootcamps or online courses
We all know the old saying “knowledge is power.” As data science continues to increase in popularity, more and more educational resources and opportunities pop up online. From books to bootcamps to free or reasonably-priced online courses, make it your goal to immerse yourself in information before beginning your search for a data science job. Meet-ups and networking events can also be a good way to meet established data scientists who can share career advice and help you identify any gaps in your skill set or resume, as well as recommend other resources you may want to take advantage of.
Consider specializing in web scraping, data visualization or NLP
High salaries and the perceived “sexiness” of data science means that competition for these jobs has increased tremendously in recent years. Given this and the vast nature of the field, it would be wise to consider picking a speciality like web scraping, data visualization, or NLP. Not only will this help you stand out to prospective employers, it will also help you narrow your focus and better hone your skills.
Add data science projects to your resume
It’s always better to show employers what you’re capable of rather than simply telling them. Of course if you’re newer to the field you likely won’t have a lot of work-related projects to share during your interview process. There are a few ways to address this. The first is by looking for ways to incorporate data into your current role. Are there ways to leverage the data your company already has to make better and more informed decisions? It’s a small step, but it can help to tie your existing experience to your desired data science role.
Another option is to work on a passion project outside the office, like this data scientist, who described a project on Medium in which he wrote a script that monitored real estate websites and then emailed him the best deals in real-time. Coming up with something that excites you and gets you motivated to code in your spare time will help create tangible work examples that you can show a prospective employer.
Lastly, once you have a baseline of experience, consider looking for freelance or even volunteer work for a small company or early stage startup that can’t afford to hire a full time data scientist.
Data science is a growing field, and it won’t slow down anytime soon. The generous salary and job security aside, in a data science role you’ll have the ability to tackle complex problems and make a large impact on your organization, making it a rewarding career path.
So, if you’ve been considering this field, there’s no time like the present. Take steps to refine your skills, increase your education, choose a specialty, and develop some passion projects, and you’ll be on your way to finding a job in this in-demand field.