In the past few years, a plethora of data science job roles have originated, but it’s really difficult to differentiate among them distinctly. Besides, the skill set required is different for different data science job roles. For big data aspirants, fortunately, the job market had kept hot in the past several years, given the unprecedented rise in the generation of digital data. As a matter of fact, 90% of all the world’s data has been produced in the last two years alone. Herein, we will be discussing different job profiles that are popular at the moment in the data science domain, and the work associated with them.
The post will help reveal the job descriptions for many data science roles for which hiring is ‘on’ in the global job markets.
Top data science skills in U.S. 2019
This job position specifically has a lot of scope for Hadoop specialists. A big data architect is needed at an organization that would like to develop a big data environment within its premises, or maybe, in the cloud. Such professionals act as a link between big data scientists and the data science needs of the firm. They are responsible for handling the full life process linked with a Hadoop solution. The typical job duties comprise generating requirement analysis, data science app development and design, platform selection, drafting technical architecture, testing, and application of a proposed solution.
Data Visualisation Specialist
A data visualization specialist is an individual who is held responsible for editing and creating visual content that comprises graphs, charts, and maps. The job involves transforming data into presentable visuals. In this role, you offer technical, editorial, and visual instructions. You help make data tell a story about itself.
Big data professionals in this job position make sure that the huge data frameworks are interconnected and architected. Here are a few of their primary job responsibilities:
- Decide about the auxiliary requirements of databases through careful probing of programming, applications, and customer tasks.
- Generate database architecture through effective planning of the proposed framework.
- Instate database frameworks by producing flowcharts, and deploy suitable access methodologies.
- Handle database execution by settling application advancement challenges, identifying perfect qualities needed for parameters, incorporating and assessing new discharges, responding to client issues, and finishing support.
- Offer database assistance using coding utilities, and answer questions asked by clients.
A big data career as an AI developer requires you to find ways to incorporate deep learning, machine learning, and artificial intelligence concepts into business processes to make the business more profitable. The skills needed for the job comprises hands-on experience in coding & programming, and familiarity with specific coding languages such as Python, Java, C/C++, Prolog, and Lisp, among a few others. As a data science aspirant, try learning as many ML coding languages as possible, while keeping Python and R on the top.
Big Data Engineer
To achieve the data analytics needs of any organization, across industries, you need data cleansing to make it usable for business purposes. There are available a number of data engineer programs in the form of professional credentials on the web for which you can enroll online. They will help you gain industry-relevant data engineer skills faster while being cost-efficient at the same time.
Big data engineers help treat raw data to make it ready to be deployed in business processes. This person would help create and handle an organization’s big data infrastructure and related tools. He is a big data professional who understands how to bring in business results from using huge pools of digital data quickly. There is no standard definition available for this specific job role, as it changes in different companies based on their specific business needs. Many times, the job role of a big data engineer coincides with that of the job role of a data scientist.
The latest Deloitte study has found that the global business landscape would need 3 million data scientists by the end of the year 2021. The extensive skillset needed for this multidisciplinary job role includes business keenness, a great logical aptitude, knowledge of programming & coding, specialized software engineering capabilities, familiarity with applied statistics and mathematics, data visualization capabilities, and experience with data analytics.
A data scientist, basically, helps transform raw data into actionable insights which is what businesses need to achieve growth and profitability. These big data professionals analyze and interpret data coming from varied sources to find solutions to business issues. Data scientists also execute on data modeling tasks in order to develop predictive models and algorithms.
Ending Thoughts – The above-mentioned job roles are among the most popular and high-paying among all that exists in the said domain. Hope you would have got a gist of their work profiles after having read this article. Best of luck!