There is an abundance of opportunity right now in data science. The growth rates are projected to continue rising into the next decade. Additionally, the amount of people expected to enter the field is projected to rise. A portion of that rise will be due to the increase in the number of higher education institutions offering data science degrees. 

What does this mean for you as an established professional turning to data science?

Right now you are entering data science at a pivotal point in the field's history. Companies are struggling to find professionals who possess the right mix of data science skills and experience outside of data science (domain knowledge). Currently, there is no typical journey to data science. The field is full of individuals with various backgrounds. There is no right or wrong way to do your career. There is no right or wrong way to become a data scientist.

There are a few traits that I believe all data scientists possess (many of which I talk about in How To Become a Data Scientist):

• a curious mind
• an instinct for finding, recognizing, and explaining patterns
• patience (lots of this one)
• an interest and familiarity with process design / development (or, willingness to learn)
• a love for data, technology, and communication
• communications (this one listed twice because it is extra important)

Take this as a message for mid- and late-career professionals. You are often excellent candidates for data science roles. Even though you have not formally studied data science. The data science degress that are available now weren't available when we were in college.

With your previous experience and domain knowledge of all things in your previous field, you are the ideal data science applicant. In addition to the above traits, you possess something that is extremely valuable to companies, and that is domain knowledge. Domain knowledge is the knowledge that you gained from all of your years in the workforce. It encompasses your knowledge of your previous field of work and it is so valuable because, based on your previous experiences, you are able to identify potential problems that others might miss.

How do you use your domain knowledge to enhance your data science skills?

The data process guides the practice of data science. While there are many different versions of the data process, the one that I use and teach begins with asking questions or specifying a business problem to solve. Now, here comes the part where you can see the value of having domain knowledge.

Since you have domain knowledge, and you have years of experience, you are already aware of potential problems. Step one done. As a mid- and late-career professional, you will know how to ask informed questions data related work. If you did not possess the domain knowledge that you have, it would be more difficult to start your data process with an informed question. Those who are not seasoned professionals like you often struggle asking good questions or specifying meaningful business problems to solve.

All of this background hard won domain knowledge is why as an established professional the overall broad set of skill, technical competencies, and other abilities can make you an ideal data science applicant!

For more on domain knowledge and the data process and how to apply it to your practice check out Chapter 2 of How To Become a Data Scientist: A Guide For Established Professionals.