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Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021

Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
  • According to Burning Glass Technologies, the two tech job skills paying the highest salary premiums today and in 2021 are IT Automation ($24,969) and AI & Machine Learning ($14,175).
  • The average salary premiums for the most in-demand tech skills range from $4,204 to nearly $25,000.
  • Startups valued at $1 billion or more are 33% more likely to prioritize one or several top ten tech job skills in their new hire plans versus their legacy Fortune 100-based competitors or colleagues.

These and many other fascinating insights are from Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech, Burning Glass Technologies’ latest research study published earlier this month. Their latest study provides pragmatic, useful insights for tech professionals interested in furthering their careers and earning potential. Burning Glass Technologies is a leading job market analytics provider that delivers job market analytics that empowers employers, workers and educators to make data-driven decisions. 

Using AI To Find The Most Valuable Job Skills

Using artificial intelligence-based technologies they’ve developed, Burning Glass Technologies analyzed over 17,000 unique skills demanded across their database of over one billion historical job listings. The study aggregates then define disruptive skill clusters as those skill groups projected to grow the fastest, are most undersupplied and provide the highest value. For additional details regarding their methodology, please see page 8 of the report.

The research study is noteworthy because it explains how essential acquiring skills is to translating new technologies’ benefits into business value. They’ve also taken their analysis a step further, providing technical professionals with additional insights they need to plan their personal development and careers.

Key takeaways from their analysis include the following:

  • IT Automation expertise can earn technical professionals a $24,969 salary premium, the most lucrative of all tech job skills to have in 2021. Burning Glass Technologies defines IT Automation as the skills related to automating and orchestrating digital processes and workflows. Six of the ten job skills are marketable enough to drive technical professionals’ salaries above $10,000 a year. At an average salary uplift of $8,851, proactive security (cybersecurity) job skills’ market value seems low. Future surveys in 2021 will most likely reflect the impact of the SolarWinds breach on demand for this skill set. The following graphic compares the average salary premium by tech job skill area.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Software Dev. Methodologies (DevOps) expertise is the most marketable going into 2021, with 634,600 open positions available in North America based on Burning Glass Technologies’ analysis. Employers initiated 1,714,483 job postings requesting at least one disruptive skill area between December 2019 and November 2020. With each skill predicted to grow at least 17%, technical professionals have several lucrative options for their personal and professional development plans. The following graphic compares job openings by skill areas for the time frame of the study:
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Quantum Computing, Connected Technologies, Fintech and AI & Machine Learning expertise are predicted to be the fastest-growing tech job skills in 2021 and beyond. Demand for technical professionals skilled in building and optimizing quantum computers and their applications will be in high demand for the next five years based on the study’s findings. Connected Technologies refers to skills related to the Internet of Things and connected physical tools and the telecommunications infrastructure needed to enable them. Fintech skills are related to technologies, including blockchain and others, that make financial transactions more efficient and secure. The following graphic compares the top ten tech job skills predicted to grow the fastest in 2021.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • AI & Machine Learning, Cloud Technologies, Parallel Computing and Proactive Security (Cybersecurity) are the most distributed across industries, translating into more diverse job opportunities for technical professionals with these skills. Professional Services leads all industries in demand for nine of the ten tech job skills, except Parallel Computing, the most in-demand skill in Manufacturing. Factors contributing to Professional Services leading all industries in demand for technical job skills include the following factors. First, their business models need to continue pivoting fast to stabilize during the pandemic. Second, better risk and compliance controls of remote operations are urgently needed. Third, better visibility into services costs across all systems to ensure financial reporting accuracy is a must-have, according to the CFOs I spoke with regarding the survey results. The following graphic compares demand for tech skills by industry sector.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Demand for AI and Machine Learning skills is growing at a 71% compound annual growth rate through 2025, with 197,810 open positions today. Technical professionals with job skills in this area see salary premiums of $14,175. Top positions include Data Scientist, Software Developer, Network Engineer, Network Architect, Data Engineer and Senior Data Scientist.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  •  Positions requiring IT Automation job skills are predicted to grow 59% over the next five years and have 282,380 positions open today. Besides being the most lucrative job skillset to have, IT Automation job skills lead to positions including Software Developer, DevOps Engineer, Senior Software Developer, Systems Engineer and Java Developer or Engineer.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

  • Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
  • It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
  • Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look Beyond Resumes To Win The War For Talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

  • Career Growth Bell Curve – Illustrates how a given candidate’s career progressions and performance compares relative to others.

  • Social Following On Public Sites –  Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

  • Highlights Of Background That Is Relevant To Job(s) Under Review Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

  • Recent Publications – Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

  • Professional overlap that makes it easier to validate achievements chronicled in the resume – Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

  1. Resumes are like rearview mirrors that primarily reflect the past. What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.
  2. By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in. The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.
  3. It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue. Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.
  4. It’s time to take a more data-driven approach to diversity that removes unconscious biases. Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.
  5. Reduces the risk of making a bad hire that will churn out of the organization fast. Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

 

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