How To Land the Right Data Science Job for You

According to statistics by Raconteur, by 2025, the world will produce upwards of 463 exabytes of data each day. While data storage capacity is expanding at a rate of nearly 20% each year, today, only a fraction of the data collected is effectively used. 

At the same time, enterprises are becoming increasingly more aware of the importance of leveraging this information and using it for business purposes. That is why Data Science Jobs are growing at an unprecedented rate and have been constantly ranking among the top-3 of Glassdoor’s 50 Best Jobs in America. 

While the benefits of undertaking a career in data science are undeniable, it can be challenging to take the first step in this field. If you are unsure about where to start, check out this guide by Career Alley to help you land the right data science job for your career goals. 

Explore Possible Pathways – And Set Your Expectations

While the term “data science” dates back to the 1970s and the early approach of computer science pioneer Peter Naur, data science only started to develop as an independent discipline in the early 2000s. 

Being so young, the industry is growing and developing rapidly, and the possibilities it offers are nearly endless. That is why, today, there are many pathways that might suit your career goals. Some job roles to consider include:

• Data scientist 
• Data analyst 
• Statistician 
• Data engineer
• Machine learning engineer

According to Payscale, in 2022, the average base salary for data scientists is $97,038, with more experienced professionals earning upwards of $136,000 per year. Some of the industries interested in hiring data scientists include:

• Financial institutions
• Healthcare
• Government organizations
• Tech enterprises and start-ups
• Public administration
• Automotive
• Cybersecurity 

Build a Solid Foundation

The first step towards landing your dream position as a data scientist is to build a solid knowledge base. Aside from being familiar with essential concepts of statistics, probability, and maths, some must-have skills for data scientists include:

• Cloud computing 
• Statistical analysis
• Machine learning
• Deep learning
• Relational and non-relational databases
• Data visualization and data wrangling
• Programming – especially in coding languages such as Python, R, and SQL
• Big data

Acquiring these skills is important, but it is just as essential to find a way to showcase your knowledge in the eyes of potential employers. You can do so by obtaining a relevant degree or investing in accredited courses. 

Obtain a Relevant Degree

A degree isn’t necessary to become a successful data scientist. However, most professionals in this field will have a relevant degree or post-secondary education. If you are unsure about the best bachelor’s and master’s degree to achieve your career goals as a data scientist, consider these options:

• Statistics, engineering, or maths
• Computer science
• Programming 
• Software engineering 

Invest in Accredited Courses

If you are not looking to return to college or you hold a degree in another field, you might consider filling any gap in your skillset by investing in accredited courses. The most sought-after and prestigious ones include:

• Cloudera Data Platform Generalist Certification
• Data Science Council of America (DASCA) Senior Data Scientist (SDS)
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate
• SAS Certified Data Scientist

The right accredited course can also help you advance your career, and move from a junior to a senior position. Specialized courses can also help you access cloud security jobs and data engineering jobs.

Gain Real-World Experience

While gaining a solid knowledge foundation is essential to kick off your career as a data scientist, employers will also be looking for real-world, hands-on experience. However, if you are looking to land your first entry-level job in data science, the chances are that your experience will be limited. 

Aside from looking for an internship, there are some other alternatives that allow you to provide your future employers with the evidence they need, without spending years in low-paid work experience. 

Check out the video below or find out some alternative options in the next sections.

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Open Source Projects and Collaborations

The world of IT thrives on open source communities and projects. Working on such projects allows you to collaborate and network with other data scientists, as well as take pride in the pieces of code that millions of people will be using.  

Build a Portfolio 

From creating a portfolio website on Squarespace to using GitHub free services or your LinkedIn profile’s “Project” section, make sure to make your portfolio, experience, and successful projects accessible to your potential employer.

Self-Led Projects

Working on personal projects alongside your current job can help you develop your skills at your own pace. 

You could start with simpler but more relevant projects which are perfect to help you build up your portfolio. At the same time, working on personal projects allows you to develop technical abilities alongside problem-solving and lateral-thinking skills. 

Sign Up for Coding Challenges

Coding challenges and hackathons are a great way to move from the theory to the practice of data science. During these competitions, you can network with other professionals in the industry and put your skills to the test by building practical applications. 

Learn Where To Find the Right Data Science Job Opportunities

Once you are sure that there are no gaps within your skillset and you have the necessary experience to be an asset for your future employer, it is time to look at where to find the right job opportunities. 

Here are some alternatives:

• Online job boards – specialized online job boards are a great starting point, as this is where most employers make listings available. While Indeed and Glassdoor might be popular options, make sure to focus on IT- or data analytics-specific job boards, including Motion Recruitment.
• Use social media to network – from LinkedIn to Facebook and Twitter, social media platforms offer many opportunities for networking with professionals in the data science field. 
• Look at the “careers” page of a company’s website – data scientists are highly in demand across a variety of industries and employers. However, if you have your eyes set on a specific company, keep an eye on any internal opportunity that might become available. 
• Join physical and online events – from industry conferences to social media lives, make sure you are attending as many events as possible.

Continue Learning About Data Science

The field of data science is in continuous evolution. No matter whether you are interested in python jobs or more general DevOps jobs, training as a data scientist can open up endless career opportunities. 

However, don’t forget that, because of the rate at which this field is evolving, your skills can soon become obsolete. So, continue learning about data science and make sure to keep your skillset up to date!