Top Roles in Data Science and AI

A great data science team brings together people from different disciplines. It cannot work well with identical data science professionals with the same levels of education and experience as each other.

What helps is a coming together of diverse perspectives and attitudes, making for a team that works well. And in the technology-driven world of today, tons of data are being generated and must be analyzed, for which the right professionals are needed. Data is the fuel for modern business, and data science professionals are engaged in a variety of jobs encompassing the gathering, arranging, and examining of such data.

The US Bureau of Labor Statistics (BLS) estimates 11.5 new jobs in data science by 2026, implying bright prospects for a data science career. Here are the top roles on offer:

Data engineer

To work with data, the first thing needed is the ability to gather that data, as only then can it be analyzed. At a small scale of operations, data engineering requires putting numbers into a spreadsheet. However, when scaled up to much higher levels, it is a very sophisticated role, involving some complicated aspects of delivering data for the rest of the team to work on.

Data engineers deal with implementing, testing, and maintaining elements of the data infrastructure dealing with data flow and design. Their work is essential for artificial intelligence (AI) analytics and machine learning (ML) handled by other members of the team.

Data analyst

A professional whose data science career involves discovering the best way to use data to solve problems and answer questions works in the role of a data analyst. Data must be properly collected and interpreted, it has to be exhaustive and relevant, and the analytics results from the data must also be interpreted. When looking for data analysts, companies place a priority on storytelling and visualization skills, to turn a pile of numbers into insights that are relevant and tangible. More than writing great, extensive, and robust code, what is needed is the ability to quickly cull insights from vast quantities of data.

Data scientist

A data scientist is a data science professional well versed in ML, statistics, and analysis. The definition varies as per the employer, so this role may involve just one or two of the aforementioned responsibilities at different firms.

The data scientist is tasked with solving business problems through the techniques of data mining and machine learning. This involves, among others, preparing and cleaning the data as well as training and evaluating the model. Data scientists take a data-driven approach to solving business problems, through extrapolation and sharing of implicit data insights. They bring data science together with analytics, data modeling, math skills, and statistics, as well as strong business acumen.

ML engineer

An AI or ML engineer is not just meant to comprehend the workings of algorithms. Building algorithms is a task for researchers, while the ML engineer uses the algorithms to work with and churn datasets by leveraging their expertise at code wrangling. Apart from coding skills, the person must be able to handle roadblocks and failures, as data may successfully pass through multiple algorithms but suddenly fail at one. Instead of perfection, what is needed is to try different approaches and find the best solution. To succeed in this role in a data science career, the person combines skills in modeling and software engineering as well as in probability and statistics.

To improve prospects in data science, it is helpful to choose to get certified with one of the best data science certifications. Certification shows the person is not just desirous of growth and higher responsibilities but also has the most current skills and knowhow to take these on successfully.