According to Burtchwork’s 2017 study of job conditions for analytical professionals, data scientists are defined as individuals who “… apply sophisticated quantitative and computer science skills to both structure and analyze massive stores or continuous streams of unstructured data.” This is perhaps the most succinct definition of both the data scientist’s role and how they differ from other data science professionals. While all data roles require using data in new and innovative ways, no other role, except perhaps the data engineer, is required to do it on quite the same scale as a data scientist.

Because of the size and scope of the projects that data scientists work on, they will frequently have skills in common with other data science professionals. Depending on the size and maturity of their team and the nature of the company, they may even be required to take on those roles in addition to their normal duties, but typically data scientists are able to support, and be supported by specialized data analysts, data engineers, etc.

What You Need To Know

Data scientists are highly educated as a group. Fully 88% of data scientists have a master’s degree and 46% have a Ph.D. Almost all of these individuals have a technical degree, with the bulk being in statistics, math, computer science, or some sort of engineering, with the rest typically being in the hard sciences. A lot of this is due to the lack of individuals with a background in data science.

With the demand for data scientists today at the highest level it has ever been, the market is beginning to produce degrees that are more specifically geared to teaching what a data scientist needs to know. While this is not significantly impacting the ability of non-traditional candidates from getting a data scientist position if they have the proper know-how, it is reducing their ability to get top positions at the best companies. To get these positions you need to have the right background, which frequently means the right sort of degree.

These degrees will teach you core skills such as:

Distributed Computing
Data scientists deal with data sets that are difficult for a single computer to manage, and instead require either a local distributed network or cloud resources in order to properly utilize them. The particular tools associated with distributed computing are changing over time, with hadoop being popular in the past, and Apache Spark and Amazon Web Services becoming more important as time goes on.
Machine Learning
Machine learning is what allows a data scientist to transfer your core knowledge of distributed computing into the results or products your company needs. There are some core algorithms that it pays to be familiar with, but it is also very important to have the skills and awareness to identify when a new algorithm better meets the needs of a particular project and to quickly learn and take advantage of it.
Statistics
Statistics is what drive and powers the algorithms and a good program will teach you why the machine learning and distributed programming algorithms work in addition to how.
Software Development
While not software developers themselves, data scientists are frequently involved with the software development process, either through doing some part of the software development process themselves or through interfacing with actual software developers to ensure their solutions are properly implemented.

What You Will Be Doing

The sort of data-focused companies and groups that utilize data scientists typically have them in a key role, where they are focused on taking their expertise in what the company does and what its goals are and turns that expertise into effective products. This is very valuable and exciting work, as data scientists frequently end up being on the cutting edge of corporate goals and activities. Data Scientists are creatives, because being able to come up with unique data opportunities requires a great deal of creativity, but in a way that is backed up by a great degree of statistical and technical know how.

One thing to be aware of is that there is a certain level of ambiguity in the term data scientist. It is not unusual for a machine learning engineer to be also called a data scientist, for example. The expectations of what a data scientist is responsible for will vary depending on the company, with smaller companies generally having the widest scope. Generally though, the highest paying data scientist positions will be at companies that have the definition noted above.

Career Development

The most straightforward opportunity for career development for data scientists is simply to move up in responsibility at a company and work on increasingly interesting and complex projects. This can also involve increased responsibility over junior data scientists or other data science professionals, and moving into a management position also allows for the possibility to transition into more management-focused roles, such as a director or vice president of data science or even in another department or field that values the sort of thinker that typically is in a data scientist position.

In other words, this is a position with a lot of potential for movement. The skills a data scientist has are useful for a company in their specific role, but are also translatable enough to allow for other opportunities. To learn more about data science degree options and see how to start your career as a data scientist, click here.

Author: Jesse Dean