How to Get a Job as a Data Scientist
Here’s a step-by-step guide to starting your career in data science.
Data scientists are some of the most in-demand professionals today. As data continues to play an increasingly prominent role in modern business, the profession will only become more valuable. Considering this promising outlook, it’s an ideal time to pursue a career as a data scientist.
Becoming a data scientist can be a rewarding and profitable career. The Bureau of Labor Statistics expects these jobs to grow 15% by 2029, which is much faster than the national average. Data scientists made a median salary of $122,840 in 2019.
You may not need any more convincing about why you should become a data scientist, but how to do so may be less evident. Here’s a step-by-step guide to starting your career in data science.
As with most professions, you’ll need an appropriate education before you can work as a data scientist. Ideally, you should get an undergraduate degree in a related field, such as computer science, information systems, or data analytics. Most professional data scientists also have a master’s degree, typically in a more specialized area within data science.
If you already have a degree, you don’t necessarily need to go back to school for a more relevant one. You should, however, look into online programs where you can take a few data science courses. Seeking out some extra certifications and licenses will also prove helpful.
The skills you learn in your classes aren’t the only education you’ll need to become a data scientist. You should also look into learning various programming languages and seek hands-on experience. You can find plenty of books and online courses that will help you develop these skills.
You’ll need more than an education to get a job as a data scientist. Most companies will also look for tangible evidence of your skills. Mohammad Shokoohi-Yekta, a former senior data scientist at Apple, says you should be comfortable with code and applying data science over being theoretical.
The best way you can show your comfort and knowledge in this area is through a portfolio of your work. As early as you can, start getting involved in hands-on data science projects and compiling them into a portfolio. You can do this through freelance data work and pet projects in areas that interest you.
Your portfolio should feature a variety of different data science projects to showcase your versatility. You should demonstrate skills in various programming languages, industries, and project types. If you can get into any data science-related competitions, your work in those will be an excellent portfolio addition.
Once you have a relevant education and a sizeable portfolio, it’s time to start looking for a position.
While versatility is always helpful, you’ll likely have better luck by targeting a niche industry with specific qualifications and certifications. For example, all Department of Defense contractors need to meet CMMC compliance, so you could earn this certification and better your chances of getting a job with the DoD.
Remember to tailor your resume and cover letter to each potential employer. Emphasize your skills and experience that are most relevant to the specific industry and position at hand. In addition to applying to jobs through sites like Indeed, grow your network on LinkedIn and try to build a respectable online presence where employers will notice you.
You may not be able to get a data scientist position at first, and that’s okay. In fact, it may be best to apply to a related but more entry-level position like data analysis first. You can grow your career from there.
On-the-job experience is your best resource for advancing your career. In light of that, try not to be too selective about the first position you accept. If you get an offer for a steady job in a data-related field, but it isn’t your ideal position, you may still want to take it. Think of your first data job as a launching point.
Companies with between 50 and 500 employees are the ideal size for your first job in data science. At these medium-sized businesses, you’ll be able to learn from senior data scientists and have plenty of opportunities to advance. Once you start working in your first position, take the initiative by trying to work on as many projects as you can without stretching yourself thin.
The more you seek out new opportunities within your company, the more relevant experience you’ll gain. As you work, look for chances to move upward both within your current business and other companies. If you show initiative and a remarkable work ethic, you’ll advance as a data scientist before long.
It’s never too late to start a career in data science. But if you know it’s what you want to do, don’t procrastinate. You can start getting the skills and experience you need, today. Being a data scientist is far from easy, but if you follow these steps, you can enjoy a long and rewarding career in data science.
Bio: Devin Partida is a big data and technology writer, as well as the Editor-in-Chief of ReHack.com.
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