ACM CareerNews for Tuesday, September 3, 2019
ACM CareerNews is intended as an objective career news digest for busy IT professionals. Views expressed are not necessarily those of ACM. To send comments, please write to [email protected]
Volume 15, Issue 17, September 3, 2019
It Might Surprise You Where The Tech Jobs Are
Forbes, August 20
While many people assume that the majority of U.S. tech jobs are on the West and East Coast, the underlying data paints a very different picture of where the tech jobs really are. For example, since hiring costs depend so much on salaries and bonuses, managements of these firms have a powerful incentive to locate in lower cost areas where they can pay a lower wage and still buy a good lifestyle for their employees. And because of the nature of tech operations, these firms are remarkably flexible in where they are located. Moreover, tech firms hire more than just coders, and that often lead them to recruit different types of talent not easily found in expensive coastal cities like New York or San Francisco.
Contrary to conventional wisdom, the cities showing the fastest growth in tech and engineering jobs include such unlikely places as Cleveland, Kansas City, Orlando, Las Vegas and Atlanta. These cities show growth twice as fast as the national average and significantly faster than the prototypical tech hubs of Washington, D.C., Los Angeles, Boston and San Diego. According to the U.S. Labor Department, New York saw less than 1% growth in computer-related jobs last year and ranked 40 out of 53 metro areas. The same story is true of Los Angeles and San Francisco. Houston and Dallas have gained against the old hubs, as have Jacksonville, Orlando, Nashville, Detroit and Columbus, Ohio.
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Four Traits of Every Top-Tier IT Hire
Information Week, August 29
Given the growing demand for top-tier tech talent in Silicon Valley, IT and engineering leaders are constantly on the lookout for solid candidates. They anticipate which roles they will need to fill in the future and then network with a pipeline of prospective hires. On this never-ending talent hunt, IT leaders often face the challenge of determining which candidates will fit well into their existing teams. While many factors influence the hiring decision, the best hires typically have four key characteristics that position them for success.
Top IT hires are often willing to learn new technical skills. No matter what position you are looking to fill, ensure every team member can solve simple coding problems and has a willingness to continue improving technical skills. Some candidates may believe that by simply applying for senior level positions, they can bypass developing the technical skills required to do the job well. However, senior leaders need to lead by example and show their teammates they can dig deep and help solve hard problems too. It is also critical to look at past relevant experience when evaluating a candidate, and also to seek out key soft skills, like the ability to solve existing problems creatively. Finding creative solutions starts with maintaining the fluidity that enables engineers to keep pace with the current rate of technological change. Only by staying familiar with new technologies can IT workers continue to innovate.
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Artificial Intelligence Offers Jobs Beyond Data Science
Dice Insights, August 29
Tech professionals almost reflexively associate artificial intelligence jobs with data science, but the reality is that that there are many other AI-related job opportunities within the tech sector. It is particularly worth noting the dramatic growth in non-technology roles that have been touched, in one way or another, by AI. Jobs such as logistics coordinator, process improvement manager and transportation specialist are all proof that AI will create a brand-new class of products, such as self-driving cars, that will require humans to build, maintain and deploy.
For tech professionals, some of the best opportunities are found not in data science or the software development that enables it, but in the design, engineering and management of systems that support it. Technology is not the only challenge involved with implementing AI solutions, experts say. Budgets, a lack of skilled talent, uneducated executives and, increasingly, complications from tying together different functional systems are driving demand for new types of expertise. Since the amount of data being used by businesses continues to grow on all fronts, current business models are sure to expand in size and complexity until they break. That means you have to be developing alternative data models while the current ones are running in order to be prepared for such an eventuality. At the same time, you have to be assessing the health and configuration of all those systems.
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How Big Data Is Changing the Job Market
Inside Big Data, August 29
Big data is changing the nature of virtually every industry and recruitment and hiring processes are no exception. Big data has had a significant impact on the way candidates look for jobs, as well as on how employers make hiring decisions. For job applicants, big data is especially useful when it comes to understanding the inner workings of a company. Big data can help to prevent a candidate from choosing a job that is not really right for them by giving them insights into corporate culture and how they would fit into such a work environment.
Big data can be a valuable tool in resume building. After all, only 2% of applicants get an interview. Basically, this means that a candidate has to grab the attention of a recruiter as quickly as possible. To do so, a candidate needs to know exactly what an employer is looking for and tailor his or her resume to those requirements. This is where big data comes in handy. Candidates can use big data and analytics to learn about company culture and improve their search based on their findings. Scouring employment databases and job sites that have large data sets can help candidates compile a resume that is likely to stand out. Big data also aids candidates in finding major selling points for their target companies. This can, in turn, be used in their resume summary. The summary is often the first thing recruiters look at, so using big data is crucial in this regard.
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Want to Become a Video Game Developer?
Built In, August 23
While there is no established path for breaking into video game development, there are several steps that anyone can take to maximize the chances that a hiring manager will notice their unique background and skills. Based on interviews with top game developers, it is clear that one of these skills is the ability to master complex programming languages very quickly. Another is the ability to self-direct your own professional development in a competitive job market, keeping in mind the set of ever-evolving tools used by game developers. Of course, merit matters, too, and applicants with strong portfolios and demonstrated skills will stand out from the pack.
The job market outlook now for video game developers is very competitive. Companies are looking first and foremost for anyone with experience. For any new position that is posted, there are a lot of applicants, many of which just are not qualified. If you have the right talent and you are doing the right things, your resume is going to stand out quite prominently. Do not assume, however, that your computer science degree will be the main selling point of your job application. You need to show a willingness to improve your skills on your own time and an ability to adapt pretty quickly to evolving market conditions. There are definitely plenty of people playing video games and buying video games, so there will not be any shortage of jobs, but you have to do everything you can to make your resume stand out. There are a lot more people looking for entry-level roles than there are entry- level roles available. Thus, having a portfolio website definitely helps.
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Breaking Into Cybersecurity Careers Through Nontraditional Paths
Tech Target, August 28
Within the cybersecurity sector, hiring processes tend to focus too much on certifications and traditional experience while overlooking candidates who are capable, enthusiastic and willing to learn. Yet, there are plenty of nontraditional paths to a cybersecurity career. By networking with other industry professionals, asking questions of seasoned veterans in the field, and committing to a program of self-study and self-improvement, it is possible to break into the field. Given the talent gap in cybersecurity, companies are often just looking for people with the right mindset who are easy to train.
Based on the experiences of those attempting to transition into cybersecurity careers, being successful in this endeavor does not require a specific path or background. Many real-world skills can be transferred into cybersecurity. The field is so massive that there is literally something for everyone. If you want to learn and you are open-minded, even if you do not have a security background or IT background, you can do it, but you have to figure out how to discover the unknown and develop the hacker mindset. That kind of mindset development and training is hard, but when you get it, it is incredibly powerful. The keys to success are in understanding who you are, what you are good at and how it can translate, identifying what cybersecurity skills to work on and being willing to learn and connect with others. When you network, show others your passion. If they like you, they will be willing to refer you into companies and then talk to you about positions that they know that are opening up. Really finding people not only that can mentor you, but also that are willing to help you figure out your path and help mold you into the next cybersecurity leader.
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Skills Are Critical in Data Science Job Hunt
Datanami, August 22
Demand for data scientists continues to outpace supply, which is leading more job candidates than ever before to consider a career in data science. In fact, data scientist is still the number one job in the country, according to 2019 Glassdoor rankings, with a median base salary of $108,000 and more than 6,500 job openings nationwide. Moreover, the number of jobs for data scientists is projected to grow nearly 20% from 2016 to 2026. While an advanced data science degree can definitely help during the data science job hunt, having the right skills is a more critical factor in landing your dream job. Often, employers do not care how you obtained those skills, as long as you have them.
An advanced university degree has traditionally been thought of as the key to landing a data science jobs. Having a PhD in data science (or similar fields) has typically been an indicator of future success. Coming from these university programs, you will undoubtedly have taken courses in statistics, mathematics, machine learning, computer science, and programming. If your data science education came within a computer science department, you probably have familiarity with distributed and parallel systems. These are fundamental building blocks for data scientists, and form the foundation for their work. The degree will prove that you have mastered these core concepts required to be a data scientist. However, as the pace of innovation in data science has accelerated, degrees have lost a little bit of their punch in the job market. Employers are now looking for data scientists with a certain set of skills, and it does not necessarily matter how you got those skills.
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Is Going To Coding Boot Camp Worth It?
The Globe and Mail, August 30
Non-degree boot camps have popped up in major cities and online where novice computer coders learn the basic skills needed to apply for jobs as junior developers working on apps and websites. These courses last just over 15 weeks on average. According to the 2019 Market Sizing Report from Course Report, more than 23,000 students were enrolled in North American programs in 2019, 11 times more than in 2013. Though ads promote boot camps as a simple way to change careers and increase salaries in only a few months, they are not a magic fix. But for students willing to put in the work, and who can manage the almost $13,600 average price tag, there can be long-term rewards on the other side of graduation. Typically, boot camp programs with selective admissions processes tend to produce better results for their graduates.
For those graduating from coding boot camps, the competition is often tough. Graduates compete against job hunters with computer science degrees and others who have coded since a young age. While top boot camp students do well on the market, others can become disillusioned with the time that it takes to find a new job. There can also be tradeoffs involved in the new career. A flexible schedule and a higher salary can also mean working longer hours and late nights. Before applying to a coding boot camp, then, it is important to do some due diligence about the placement rates for new graduates and what types of job opportunities will be available.
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Velocity in Software Engineering
ACM Queue, July 29
Software engineering is necessary in all modern companies, but software engineers are expensive and in very limited supply. As a result, there is a lot of interest in the increase of velocity from existing software-engineering investments. In most cases, software engineering is a team activity, with breakthroughs typically achieved through many small steps by a web of collaborators. Good ideas tend to be abundant, though execution at high velocity is elusive. The good news is that velocity is controllable and companies can invest systematically to increase it. Moreover, velocity compounds and is also habit-forming, pushing team members constantly to raise the performance bar.
Velocity is a function of direction and speed. Of the two, direction is more easily overlooked. The most common reason that projects fail is that the team was building the wrong thing. Inherent in the development process needs to be the clear identification of who the customers are. This enables team members to work backwards from their needs to a product definition that would viably meet those needs. Success is not delivering a feature. Rather, success is learning how to solve a problem of a customer. Teams often lament that customers only use 20 percent of what they shipped. Ideally, teams should listen to customers and meet their needs while shipping only the 20 percent that most interests them. Yet, even for the best listeners and most visionary innovators, it is difficult to predict what customers need. Because there is some guesswork involved in choosing a direction, flexibility and course correcting become crucial. Flexibility might show up as openness, maximizing the rate of experimentation, learning quickly, reducing commitment to any given plan and rapidly evolving products.
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The Long Game of Research
Communications of the ACM, September 2019
In the tech industry, research is a long game in which patience and endurance are necessary components. Quite simply, seminal research work in an area like data mining might go underappreciated for years before companies figure out the right application for it. In short, there is a dangerous tendency in the corporate world to forgo pure curiosity in favor of alleged pragmatism. Moreover, there is no single formula for successful research. Sometimes it makes sense to focus short-term on an immediate problem, but, quite often, dramatic breakthroughs are obtained by viewing research as a long game.
Quite simply, research planning and strategy in the tech industry is so hard because we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. Consider, for example, the Gartner Hype Cycle, which is characterized by the peak of inflated expectations, followed by the trough of disillusionment, then the slope of enlightenment, and, finally, the plateau of productivity. This describes how new technologies enter the mainstream. Early buzz is often followed by disillusionment. In many fields, conceptual and engineering breakthroughs that took place years ago lay the foundation for future applications. This is especially true in the area of AI. In recent years, deep learning methods have been responsible for astonishing breakthroughs in computer vision, speech recognition, natural language processing, and robotics.
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