Tips for Positioning Yourself as a Data Scientist

The growing interest in the job role of a data scientist has many IT professionals looking to make a change in their career. The position has been evolving so rapidly, however, that many companies are struggling to understand the responsibilities and; therefore, are not able to create well-targeted job descriptions.

Until the data scientist role becomes more mainstream, these are just some of the challenges that you’re likely to face.

When considering how to position yourself in your resume, think not only about your technical skills, but also consider your aptitude, interests, and characteristics. I’ve compiled a list of those skills, qualities, and characteristics that are essential for a data scientist to operate successfully.

  • Programming Capability – It doesn’t matter in which languages you’ve previously programmed, as long as you’ve excelled at them. Your aptitude and experience are much more important.
  • Data Literacy / Data Management – As Big Data has emerged on the scene, data management has become far more complex. Being able to work with both structured and unstructured data and their associated architectures creates a great opportunity to move into this field.
  • Intellectual Curiosity – Some people are born just naturally curious about the world around them. They are the ones who will continue to explore and discover why things are “as they are.” Data science is a great fit for these types of people.
  • Business Acumen – Everyone in IT needs to understand how a business functions and its overall goals and strategies. For the Data Scientist, this goes far beyond comprehending the internal processes and practices; it extends to the business drivers and economics that truly impact a business.
  • Communication – A data scientist needs to be able to communicate well verbally, as well as in written form, including utilizing data visualization to visually represent information with a compelling message. Being able to use sophisticated data visualization tools is a necessary skill to have in their toolbox.
  • Advanced Mathematics and Applied Statistics – Data scientists love math, as well as statistics, but they are not merely mathematicians and statisticians. They are able to blend their knowledge of the business with their natural curiosity in order to provide insightful analysis.
  • Design and Engineering – As an experienced IT professional, you will want to highlight your design and engineering experience.

In beginning to understand the role of a data scientist, it is equally important to understand how it differs from many other IT job roles. A data scientist may perform the role of a data analyst or even a data wrangler, but this is not their primary function. Instead, they blend their very inquisitive nature with strong domain expertise, along with their knowledge and experience in mathematics, statistics, and machine learning to enable perceptive decision making.