August 18, 2022
By Taylor Graf
STEM Job Essentials: How to Secure a Job as a Data Scientist
STEM Job Essentials: How to Secure a Job as a Data Scientist
Data science is an exciting and evolving career field. Data scientists collect and analyze large data sets to report on research findings and help their organization make informed decisions. These individuals play an important part in businesses’ shift to big data and have a positive career outlook as data insights become increasingly important to companies across all industries.
In fact, the U.S. Bureau of Labor Statistics projects employment growth of 31.4 percent for data scientists between 2020 and 2030, estimating about 19,800 jobs opening in that timeframe. In addition to the estimate growth of this field, data scientists are also compensated well, with some of the highest starting salaries in the technology industry. A recent Burtch Works Study, reported that the median starting salary for data scientists is $95,000 annually. Mid-level data science professionals have median annual earning of $130,000 and experienced manager-level data scientists earn a whopping $250,000 per year on average.
With a positive career outlook, growth opportunities and high salaries, a career in data science is extremely desirable. So, how do you go about starting a career in data science? Read on for our tips to securing a job as a data scientist – no matter what stage of your career you are in.
How to Secure a Job as a Data Scientist
For those getting started on their college journey, there are a number of options to set yourself up for a career in data science following your graduation. Unsurprisingly, the most relevant degree choice would be in data science/analytics if your school offers it. However, if your school does not offer this specific major, relevant coursework could also be found in statistics, computer science and mathematics degrees. In some cases, there will be a specific track for data science within these majors, or, minor options may also be available to tack onto your degree. In any case, internships in data science, data analytics, machine learning or software development can be useful when applying to full time data science roles following graduation.
Professionals in varying fields that want to break into a data science career, have some options to build out their resume with relevant experience. As data scientists need a range of knowledge in analytical, technical and communicative skills, it is likely that your current work experience could be transferable to a career in data. To gain specific data science education, there are many in-person and online certificate programs offered at universities across the U.S. Additionally, there are online courses available specific to hard skills that a data scientist might need, such as Python, Tableau and Machine Learning. These development opportunities not only will help you learn valuable skills to make you successful in data science, they will also help you build out your resume and stand out to potential employers.
No matter where you are at in your career, an adequate resume and comprehensive portfolio is a great way to stand out to prospective employers and demonstrate your experience in data science tools and methods. A portfolio can include relevant school projects, internship work or projects that you’ve worked on in your personal time. Your resume should include any relevant work experience and highlight data science-related skills. Consider how you can show both your technical aptitude and communication skills to a potential employer, as both are essential to a career in data science. Once you land an interview, follow our Six Tips For an Essential Interview, and be prepared for an aptitude test that may assess your technical abilities. Keep in mind that most data scientists come from a variety of backgrounds, either starting off their career as a recent graduate or come from a different career entirely. Be confident in your skills and experience and you will be well on your way to a career in the exciting field of data science.
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