As a professional in data science, machine learning, advanced analytics, or artificial intelligence, you know that domain knowledge is important. It's what you have learned on the job that is unique to your previous career. Having had the opportunity to build a rich background of domain knowledge is one of the many strengths that mid- and late-career professionals bring when they transition into data science.

Is domain knowledge really that valuable?

Remember that your domain knowledge is valuable. Domain knowledge is not something you obtain through luck or by chance. It is what you gain through sheer effort, experience, and time dedicated to a career. Finding the value in your domain knowledge is dependent on finding the type of position where your domain knowledge will be an asset.  For example, a nurse turned data science would not be as big of an asset to a major banking chain as an accountant turned data scientist would. Sticking within your scope of domain knowledge could give you a competitive edge when it comes to apply for positions.

Are there other ways my domain knowledge can be used?

Domain knowledge is critical for professionals in data science, machine learning, advanced analytics and artificial intelligence because it allows them to understand the business context in which they are working. The business context includes the soft skills needed to work with others, i.e. strong communication skills, delegation skills, problem solving, and proper follow-up. Without this understanding, they would be unable to provide valuable insights that can help businesses make better decisions. Additionally, domain knowledge allows data science professionals to communicate more effectively with non-technical stakeholders. This is essential for ensuring that data-driven decisions are made at all levels of the organization.

Can my domain knowledge really give me an edge above the competition?

Finally, domain knowledge gives mid- to late- career professionals a competitive edge when it comes to career advancement. This is because domain knowledge is not something that can be quickly learned. It often takes years of experience to develop a deep understanding of a particular industry or business process. As a result, mid and late career professionals who have this expertise are in high demand for advancement in data science, machine learning, artificial intelligence or advanced analytic positions. 

Domain knowledge is critical for data scientists who want to be successful in their field. It allows them to understand the business context in which they are working, communicate more effectively with non-technical stakeholders, and gives them a competitive edge when it comes to career advancement. If you're looking to get ahead in your data science career, make sure you're investing time and energy into building up your domain knowledge.