Navigating Career Paths in the Dynamic Landscape of Data Science

Data Science continues to be a career path that is on the rise. But what exactly is data science? It is the process of gathering analytics and information to make informed business decisions and solve complex problems. The career paths for those who choose to go into data sciences will need to focus on either the data and technical side or the business side.

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The technical path of data science will include skills like machine learning, software engineering, and data networking or processing. Whereas the business side of data science will incorporate skills in managing and deciphering data, statistics, and mathematics,

Data Science Skills

As with any job/career, a set of skills is needed to be qualified for that position. The area of data science is the same. To obtain an entry-level position in this field, a bachelor’s degree in analytical or quantitative disciplines like mathematics, statistics, computer sciences or finance will be required. Additional skills and certifications, such as SAS Certified Data Scientist, SAS Certified Advanced Analytics Professional, and CAP (Certified Analytic Professional) will be required to progress and advance in a data science career. These designations are an asset to any employee’s resume.

Data Science Careers

Careers in data science range from entry to senior-level roles. These roles vary in responsibilities and skill sets, and what is needed by each company. Examples of these careers are as follows:

  • Entry-level – taking a position at this level will center around executing analytical tasks that have been delegated from the mid or senior level. Employees will gain valuable skills in languages like R and Python, as well as learn valuable skills from day-to-day operations of data science. Job titles at this level include data scientist, data analyst, or business intelligence analyst.
  • Mid-level – Although many of the positions of a mid-level data scientist may be the same as an entry-level employee, the difference is there is an added layer of seniority and ownership at this level. At this point in your career, the employee should have a strong understanding of how to use the data in business decisions. Employees are also given the responsibility to work unsupervised, manage a team, and be responsible for the output of that team. These job titles include data engineer, data architect, and senior business analyst.
  • Senior-level – When advancing to this level, the employee must prove that they can build, manage, and lead a team with success in crisis and while leading projects. Their role is to manage the gap between technical, analytical, and business aspects of the business. Responsibilities at this level include leading projects and exemplifying strong leadership skills. The senior-level employee will also serve as a coach and mentor to mid- and entry-level team members. These job titles include chief information officer, chief operating officer, and chief data scientist.

Data Science Job Outlook

The future is bright for aspiring data science professionals. The demand for scientists and engineers continues to grow. The opportunity to add new skills and certifications as this field is ever-changing allows employees the ability to advance and change with the times.

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