There has been a lot of buzz about data scientist jobs recently. And for a good reason! Since 2016, data scientist has been at or near the top of Glassdoor’s Best Jobs in America list.

But since the job hit the top spot in the list, the field has exploded in popularity and complexity. (Our data shows that data scientist job postings have more than tripled in the last five years!) In this quickly evolving field, new opportunities are always on the horizon. Perhaps the question is no longer, “Do you want to be a data scientist?” But rather, “What kind of data scientist do you want to be?”

In addition to the core set of skills that all data scientists must master, there are many specializations within the field because business problems can be approached in multiple ways using multiple tools. A lot of infrastructure is required, especially in large companies, to store, move, and analyze data. Employers are often looking for data scientists with specific areas of focus. Depending on the industry you want to work in or the size of your team, you may need to strengthen your data visualization, machine learning, or data curation talents.

In this article, we will look at three trending data scientist jobs and what it will take to land them.

Data Visualization Engineer

Data, data everywhere and not a drop to drink. The world is awash in data, and companies need experts to handle data so they don’t drown in it. Data visualization engineers package all that fluid data into a form that can be easily digested and understood. Data visualization engineers straddle the data engineering and design worlds by marrying an understanding of the data with the ability to display that information beautifully. According to Lightcast, job postings requesting data analysis and visualization skills have doubled in the last two years.

Data visualization engineers create dashboards, charts, and other visualizations that different stakeholders in a company can use. Data visualization engineers may also use visual tools to identify anomalies in data to support data scientists as part of the data engineering process. They work with many different people in an organization and use a mix of programming and software tools to fulfill requests. They need to understand how data is structured and stored and how to access it. The role is an unusual blend of art and science that may appeal to people who don’t have the extensive computer science or statistics background required to be a data scientist.

Skills needed

In addition to an educational background in graphic design and data science, to become a data visualization engineer you will need proficiency in the following:

• Data visualization and dashboarding tools like Tableau or SAS® Visual Analytics.
• Graphic design principles and best practices.
• Coding knowledge for data visualization libraries from Python, R, SAS and Javascript.
• Leadership, communication and presentation skills.

Recommended training

If you are interested in becoming a data visualization engineer, getting experience with SAS Visual Analytics through free tutorials and other courses is a good place to start. Also, check out the SAS® Visual Business Analytics Specialist credential to prove your proficiency in designing reports and dashboards with SAS Visual Analytics.

Machine Learning Engineer

A machine learning engineer uses artificial intelligence to solve complex data problems by developing models that predict outcomes based on past behavior. A machine learning specialist or machine learning engineer is vital to a data science team. After the data sources have been identified and cleansed, the algorithms that analyze the data need to be built. This is where the machine learning specialist comes in. They develop and deploy the models that will extract insights from the data. Demand is rising quickly for this specialized skill set with over 14,000 jobs posted in the U.S. last year.

Skills needed

Machine learning specialists must still be well-versed in core data science principles because they may be consulted during the data preparation and software selection phases. In addition, machine learning specialists will need experience with the following to be an outstanding candidate:

• An educational background in software engineering or computer science.
• Programming skills in multiple languages like Python, Java, SAS, and C++.
• Knowledge of statistics and machine learning techniques.
• The ability to collaborate on a large, cross-functional team.
• Communication skills to explain difficult concepts to nontechnical people.

Recommended training

One way to get some of these skills is through the Machine Learning with SAS® Viya® course that will help you prepare for the Machine Learning Specialist exam. Check out Cat Truxillo’s curated playlist on the SAS Users YouTube Channel for some quick hits.

Data Engineer

Not to be confused with a data visualization engineer, data engineer is climbing the ranks of best jobs lists and is poised to have a breakout moment. Over 90,000 jobs for machine learning engineers were posted in the U.S. last year, making data engineer the second most popular job title requesting skills in the data science field. Data engineering is essential to building and managing the systems that store and move data. Organizations that want to invest in data analytics must ensure that the data is available, clean, and ready to use. Data engineers create the data infrastructure and work closely with data scientists and machine learning specialists to understand their needs. They may also be responsible for educating others in the company about good data practices. No longer the unsung heroes of the data department, data engineering is a great career path for those who are drawn to data management more than data analysis.

Skills needed

Data engineers need to love data and be up-to-date on the latest tools. Software engineers often have the right background to transition into data engineering, but a broad understanding of the analytics lifecycle is also important. A successful data engineer who can create and maintain a secure data infrastructure needs the following skills:

• Experience with ETL platforms.
• Advanced coding skills in database and statistical languages like SQL, Python, SAS, or Scala.
• Can solve complex data problems.
• Is a data evangelist who can explain the importance of good data governance.

Recommended training

Several companies offer professional certifications in data engineering. The Data Curation Professional learning path at the SAS Academy for Data Science is a great place to gain the skills you need to become a data engineer.

No matter where you are in your data science journey, SAS has training through the SAS Academy for Data Science and resources to support you as you become exactly the kind of data scientist you want to be. Are you working in any of these jobs and have insights to share? Let us know in the comments below.

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