Want a career in AI? Here are the skills you need
- A large number of companies are keen to pull specialist talents in artificial intelligence and machine learning solutions
- A 51% spike in job postings has been seen within the last year
Despite the dreaded, looming uncertainty felt throughout 2020, the prevalence of Artificial Intelligence (AI) has been undeniable. Over the last four years, AI specialists reportedly represent the fastest-growing role in the United States.
According to recent data from job site Indeed, jobs in AI have seen a recent explosion with a steady hike over the last five years. The report notes that AI job postings have gone up consistently over the past two years, with a 46% hike between 2018-2019, and a 51% spike between 2019-2020.
The dramatic increase in job openings within AI hasn’t gone unnoticed by savvy job seekers in the market. Since March and July of this year alone, AI-related job searches have increased by 20%, while job searches in AI have seen a significant spike of 106% since June 2019.
“(Artificial intelligence) is one of the hottest areas in the career market right now,” said senior manager in robotics and cognitive automation at Deloitte, Aoife Connaughton.
The report went on to cite that skillsets within Python, TensorFlow, and Natural Language Processing (NLP) topped the list in employer’s demands.
The right talent to fill these roles may still be lagging, however. “It regularly tops the list of most in-demand skills areas, with demand tripling in the last three years and six times more open roles than candidates available to fill them”, Connaughton added.
For anyone looking to take advantage of AI opportunities in the future of work, there are three main skills areas to focus on – technical, business analysis, and managerial.
AI skills in demand
Technical skills include how to manage and configure AI application programming interfaces (APIs) and deep neural networks (DNNs), advanced analytics, as well as data science skills. Programming expertise in Python, C#, and R, together with experience in AI-specific vendors and applications, as well as user interface (UI) and user experience (UX). Connaughton adds, “Developers and data scientists with experience using natural language generation (NLG), speech recognition, virtual agents and machine-learning specialists are most in-demand for technical roles, while finance and HR professionals with experience exploring and scaling use cases for cognitive automation are hot property right now.”
Business analysis skills to consider include process reengineering, business case development, the experience of DevOps and agile delivery, as well as vendor selection and management.
AI roles requiring managerial skills include data literacy, governance, ethics, AI architect skills, as well as experience managing IP rights between customers and vendors.
Unsurprisingly, AI is a highly scientific field that requires a great deal of education, training, and focus. That said, true upskilling in AI requires more than just training courses. According to a recent 2020 survey, a pragmatic approach that translates knowledge into real-world skills effectively develops important tech skills like creating data sets, building a machine learning model, or using Python or R notebooks.
What’s more, companies are not only keen on hiring upskilled workers but employees able to cross-skill. Specialists in one area such as data science need enough basic skills in another such as business. The ability to speak one another’s language and be comfortable in multiple worlds is a critically invaluable skill not just for collaborating on AI-related challenges, but for also deciding which problems AI can solve. A team of “multilingual” people who can integrate multiple tech and non-tech skills helps to assist non-tech employees with tech solutions and tech employees with business solutions while emphasizing the basics of each other’s skillsets.
“While there’s a pipeline of new graduates with skills and a strong interest in areas such as machine learning, demand far outstrips supply and there is a need for seasoned technical business professionals to upskill to be able to identify and exploit opportunities”, explains Connaughton.
Findings from the survey go on to suggest that as the democratization of AI continues, data scientists and AI specialists still need to closely monitor AI model development and training, data, and model governance, as well as how IP rights and openly sourced software and datasets are handled.
How to upskill
There’s a huge variety of ways to take advantage of these exciting new opportunities. It’s vital to browse through AI job opportunities to determine the requirements and specific qualifications needed. Some roles require low-level programming languages while others in the healthcare industry need expertise in data services like Spark and Blockchain.
Regardless of the type of job, the best way to know exactly what’s needed is to stay as up-to-date in the industry as possible. For anyone relatively inexperienced in AI there are two routes to consider: a formal further education such as a master’s in AI or part-time learning, as well as short online courses at low or no cost.
“Stanford’s Machine Learning course by Andrew NG is famous for its quality, accessibility for technical novices and masters alike, and its comprehensiveness,” said Connaughton.
As AI continues to integrate into the workplace in key ways, agile and adaptable organizations are moving well beyond fears of the impact on day-to-day jobs and focusing on how to capitalize on a wealth of specialist roles.