As soon as we hear the word ‘Artificial Intelligence,’ digital assistants, robots, self-driving cars, and chatbots directly strike our minds. This is what AI stands for. These are powerful, engaging, and genuine examples of AI in play, which is why it is valid for normal human beings to think of these applications when the technology is mentioned instantly.
However, unlike some other technologies, AI is nowhere near its end game. The technology will continue to grow across 2020 and beyond and will soon become the force powering the tech and corporate worlds forward.
The massive implementation of AI in the world around us will also help create exciting job opportunities and much more. According to a study by Gartner, AI is expected to make some 2.3 million jobs shortly. These jobs will be seen by the end of 2022 and will translate to some 500,000 potential new openings. While the COVID-19 pandemic saw a decline in employment in all industries, the period after it will see a sharp rise in AI jobs.
It is now evident that AI will be an in-demand skill in 2022 and beyond, which is why skilled professionals should look to make a mark here. In this article, we look at some reasons and directions to start a career in data and AI. The market is increasing, and now is the time to act and pick up your dream career.
Reasons Data Science Jobs Will Be Popular
Before we mention the skills to have for a career in AI, we will first look at some of the reasons you should start a career in this field.
Companies Need to Organize Data
The data boom of the 21st Century has seen many companies accumulate data far beyond what they can manage. The last two decades have seen a consistent shift toward electronic content creation, transactional data generation, and high streams of data logs. This now means that every organization has tons of unstructured data. Data and AI jobs will see a massive boom as organizations look for employees and ways to structure this data and make sense of it. The actionable insights generated from your data can help improve overall results.
Shortage of Skilled Employees
A study by McKinsey found that the United States will soon face a shortage of some 190,000 data scientists and managers. This shortage will be coupled with a lack of some 1.5 million managers and analysts required to understand insights from data.
The demand is now acute over the globe, specifically in countries like China and India, where the technologies and tools have rapidly increased the pace of analysis, but skilled users do not exist. To this end, Srikanth Velamkanni, CEO of Fractal Analytics, mentions, “There are two types of talent deficits: Data Scientists, who can perform analytics and Analytics Consultants, who can understand and use data. The talent supply for these job titles, especially Data Scientists, is extremely scarce, and the demand is huge.”
The X-Factor with Great Pay
Working as a data scientist comes with the desirable X factor and luxurious pay. Data scientists today are among the best-paid professionals around the globe. The average annual salary for a data scientist is well over $100,000 in the United States. The pay scale increases as you step up the corporate hierarchy.
Also, being a data scientist and working in the AI industry is considered the new cool. Top data scientists working at Google, Facebook, Amazon, Twitter, and LinkedIn are considered premiers in the industry. Harvard Business Reviews also ranked Data Scientist as the ‘sexiest job of the 21st Century.
Low Entry Barriers
Many current data scientists entering the field of AI and data come from mathematics, statistics, engineering, natural science, and computer science. There are no particular barriers to entry for individuals entering from different backgrounds since the field itself is new.
You need to have an exceptional command of particular skills to start a career in data science.
Command Over Programming Languages
Having a penchant for coding is an important skill to have as an AI scientist or engineer. The right individual should be proficient in programming languages like C++, Java, and Python. The command over these languages will help with data structuring.
Natural Language Processing
NLP is an essential subfield of AI. NLP helps computers understand and process human languages and gets them closer to humans. AI professionals should have reasonable control over libraries like Gensim and NLTK.
Statistics and Calculus
Mathematical functions are essential to understand how algorithms work. All standard deviation, Gaussian distributions, and mean are required to pick up and understand algorithms.
The right candidate for an AI job should have in-depth knowledge of the industry and its current pain points. Additionally, the candidate should be able to pinpoint areas of improvement for future growth and working.
An AI professional should be good at communication, considering how there is significant back and forth with other stakeholders in the firm. All the actionable insights generated through models in AI are specifically communicated to other critical stakeholders in the firm.
Numbers and unstructured data shouldn’t scare AI engineers. Instead, they should be able to think and determine solutions to inconclusive AI problems critically.
We’re living in an era where business processes and solutions are driven by technology. More and more professionals are looking to open up career prospects with new-age tech jobs.