ACM CareerNews for Tuesday, December 4, 2018
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 firstname.lastname@example.org
Volume 14, Issue 23, December 4, 2018
As companies take proactive steps to adapt to a tight IT job market, they are doing whatever they can to attract top tech talent. For some, that means getting a head start in filling the most in-demand roles for next year. These include data-focused and security-related positions, according to the new Robert Half Technology 2019 IT salary report. According to the report, there are 13 jobs expected to be in highest demand in 2019, including business intelligence analyst, cloud architect and cloud systems engineer.
Business intelligence analyst is the most in-demand tech job for 2019. BI analysts need experience in database technology, analytics and reporting tools. Businesses typically look for candidates with an undergraduate degree in computer science, information systems or engineering. They want to hire someone with the skills to understand the unique data needs of an organization and then communicate those to stakeholders. Coming in as the second most in-demand tech job was cloud architect. Cloud architects oversee the cloud computing strategy of a company and are responsible for deploying, managing and supporting cloud applications. Cloud architects typically have a strong understanding of multiple operating systems in addition to networking, programming and security skills. Businesses should look for individuals with a strong knowledge of cloud services such as Amazon Web Services as well as experience with automation and vendor management.
Developers continue to be one of the fastest growing and most in-demand tech professions, according to a new report from The Knowledge Academy. By 2026, more than 253,000 new software developer roles will be created, which is the most for any job in tech. The Knowledge Academy analyzed data from Glassdoor to determine the 15 U.S. jobs expected to grow the most by 2026. One big takeaway from the report is that cutting-edge technologies like AI actually have the potential to create more jobs than they replace.
While many tech workers fear the rise of artificial intelligence (AI) and automation and the impact of these technologies on jobs, those technologies are actually aiding many people in traditional positions rather than replacing them. New job titles that do not yet exist today will be commonplace by 2026. Moreover, America will see major growth for tech-related roles in non-tech industries, including finance, consulting, and retail. In other words, being an IT worker will no longer mean that you work in the tech sector.
Artificial intelligence (AI) is growing in popularity as a discipline, and the race is on to deploy it to make products and services smarter, faster, and able to accomplish tasks that humans simply cannot do. As more companies invest in AI, it is also leading to new career opportunities. As a result, there is an increasingly urgent need for experts to help build AI models, and for domain experts who are strategic and creative enough to envision ways to use AI in their specialty. In fact, the World Economic Forum reports that the fastest growing skill on LinkedIn is AI.
In the dynamic world of AI, where data science, deep learning, and machine learning are being used to improve products and outputs in fields as far apart as astronomy, health care, transportation, security, and banking, scientists and engineers are finding that their skills are highly prized in multiple career tracks and sectors. Often, those being recruited as domain experts are those with academic experience in highly technical AI-adjacent areas, such as computer science, statistics, mathematics, and data science. They are defining the actual algorithms and what the models look like. As the models have become more sophisticated, these technical AI experts have found professional paths in research and development divisions, where they conduct ongoing research as consultants as they help different groups within their company with AI needs.
In Cybersecurity Job Market Demand for Top Talent Soars
Security Boulevard, November 12
Employer demand for cybersecurity professionals across the United States continues to soar, according to a new study by Burning Glass Technologies. While the U.S. already has hundreds of thousands of cybersecurity workers, there are still plenty of positions to fill in IT departments across the nation. U.S. employers in the private and public sectors posted an estimated 313,735 job openings for cybersecurity workers between September 2017 and August 2018, according to the job market analytics firm. By way of comparison, there are already more than 715,000 cybersecurity workers currently employed around the country. On a combined basis, that means that there are over 1 million available jobs in cybersecurity in the U.S.
The study on cyber IT talent was commissioned by CyberSeek, which provides data about supply and demand in the cybersecurity realm. Among specific core jobs, the top five by employer demand include cybersecurity engineer, cybersecurity analyst, cybersecurity manager, cybersecurity consultant and penetration and vulnerability tester. The Washington, D.C. metropolitan area has the largest number of job openings for cybersecurity professionals (44,058). The other top five metro areas are New York City (20,243), Dallas (12,062), Chicago (11,201), and Los Angeles (10,589). The average salaries for core cybersecurity jobs range from $75,000 for a cybersecurity specialist to $129,000 for a cybersecurity architect.
Time for New Approaches in IT Recruiting
Information Week, November 20
Time-tested ways of finding entry-level IT workers still apply, but when it comes to hiring experienced software engineers or DevOps leaders, the current highly competitive job market means you need to get creative in who you look for and how you look for them. The danger with automated resume-sorting tools is that people who may be a great fit for a certain job are never located. That is why savvy IT leaders go out of their way to find good candidates. As you look for ways to attract candidates, find ways to have meaningful conversations with applicants and sell them on what your company is doing. You are not just sifting through online hiring boards anymore. Instead, it means showing up at events, such as hackathons and meetups, to meet with candidates and explain what your company offers.
One new approach to finding top IT talent is leveraging the power of social networks, rather than relying on traditional job boards. This often results in word-of-mouth references from IT pros already employed at a company. That can help to find some hidden gems, because sometimes you miss the people you do not even know you should be looking to hire. And sometimes there are major differences in the way people define their own skills. Sometimes that means a person who truly believes he or she is an expert is not really an expert when compared to others in the field. It comes down to a conversation or call, to find out how much they really do know.
How To Get a Job Working With Artificial Intelligence or Machine Learning
The Next Web, November 30
Artificial intelligence is one of the most exciting and attractive fields to enter for IT workers. The global machine learning (ML) market is estimated to grow from $1.4 billion in 2017 to $8.8 billion by 2022. AI is projected to create 2.3 million related jobs by 2020, according to Gartner. The average salary of a machine learning engineer is between $125,000 and $175,000. At the top ten highest paying companies for AI talent, the average salary easily surpasses $200,000. Clearly, there are a lot of reasons to join this booming industry, and the first step is simply recognizing the types of positions that are available.
There are traditionally two fundamental splits in the world of artificial intelligence and machine learning: data scientists and machine learning engineers. Data scientists help tailor the business logic of the models that are being created. Basically, data scientists help communicate findings from data models to business decision-makers and they help tune and tailor models that help businesses ask the right questions of their data. In contrast, machine learning engineers build the data framework that allows for data scientists to process and work with huge reams of data that continually updates. In practice, they are responsible for feeding the models defined by data scientists with the data they need to perform well, and they are often responsible for taking theoretical data science models and helping scale them out to production-level models that can handle the day-to-day of companies that generate terabytes of data. Even if the two broad roles share some overlap, a data scientist is often going to be working with the theory behind the data science of artificial intelligence, while machine learning engineers will implement models in practice.
7 Things Developers Should Never Say in a Job Interview
Dice Insights, November 26
In order to make a good impression during an interview, software developers should be aware of common mistakes and slips of the tongue that could hurt their chances of landing an offer. For example, disparaging a particular programming language, library or framework during a job interview shows a lack of flexibility and a potential unwillingness to learn new technologies. This comment raises a red flag because it may limit the type or number of projects a developer can work on in the future. The goal is to be as flexible as possible, because you never know what kinds of projects or opportunities may materialize down the road.
One common mistake made by developers during a job interview is making negative comments about a previous team. This never makes you look good, and could signal that you may be unable or unwilling to work collaboratively to come up with a solution to a critical issue. Not everyone agrees on what constitutes high-quality code or a great working environment. Rather than listen to you complain about the coding at your last job, hiring managers want you to describe how you helped boost quality and engineered consensus on your previous teams. Another huge red flag in an interview is when a developer immediately wants to jump ahead, either in projects or positions. While ambition is usually a positive quality, the hiring manager is usually filling a very specific role, not the role you want three years from now. Focus on what you can bring to this opportunity and how you can solve the company’s immediate needs.
4 In-Demand Skills You Can Learn Online
Entrepreneur.com, November 30
The growing abundance of online resources to learn about topics in computer science has created an unprecedented opportunity for self-taught professionals and entrepreneurs. Consistently learning new skills and adapting to the evolving work environment is crucial to maintaining a fruitful career in the modern workforce. Many college graduates do not even work in the field that they majored in, and instead, work in an area that they initially received a job in out of college or taught themselves how to excel in. Whether you are looking to transition into another field or just want to learn some new skills as a hobby, the ability to do so has never been as convenient or powerful as it is now, especially when it comes to coding and software development.
The increasing prominence of online education resources has made a huge difference in helping aspiring entrepreneurs and freelance workers advance their careers. A 2017 study by Upwork revealed that freelancers are predicted to become the majority of the workforce within the decade. Many freelancers and entrepreneurs are self-taught, learning new skills on the fly out of a need to develop a professional talent or by meticulously studying a topic through online classes or reading. The progression of the Internet into its modern form has opened avenues for extensive, user-friendly, and affordable educational material for users of all levels of experience. Online resources for enhancing professional skills range from free university courses to standalone educational platforms that connect students and paid professionals. Some even offer informative games, video tutorials and open-source frameworks for improving content.
The Importance of a Great Finish
ACM Queue, November 19
In order to boost your value as part of an IT project team, it is important to finish strong and keep up the momentum of any project until the very end. The reality is that, in the IT world, not all work is created equal. It is often the work through the bulk of a project that is not remembered or recognized. The work that tends to be remembered from any given project is the work that happened last. It is the final step that most people will think of, because it happened most recently. This is especially true of the people who have the most power over your promotions and future career opportunities, who do not see what you accomplish on a daily basis. They just see the results, which is why it is important to focus on having a great finish to a successful project.
One reason why a strong finish is so important in IT project work is based on simple human psychology. Humans tend to remember the ending of something far more clearly than any other part, even if other parts were more significant or important. Essentially, our brains can process only so much. We take in so much information every day that it is impossible to remember everything completely. As a result, our brains have to give priority to certain pieces of information over others. This means that we usually have the clearest recall for things that were associated with strong emotions and things that happened most recently. This is known as the Peak-End Rule. At work, for example, performance reviews are usually weighted toward the work you did most recently.
There Are No Digital Humanities
Blog @ CACM, November 26
Digitalization and the digital revolution may seem entirely new, but there is a long historical record of digital calculation that extends back for hundreds of years. This requires a new way of thinking about digital, since many assume that the opposite of digital is analog or mechanical. Historians sometimes speak of a pre-digital era. Even museum experts are surprised when historical mechanical calculating machines are described as digital. For them, digital and electronic are synonymous. However, simply equating digital with everything new, and analog with everything old, does not work.
Digital is not an achievement of the twenty-first century. For example, the abacus is regarded as the oldest digital calculating aid. The Romans also used digital bead frames. Similar devices are still offered today at flea markets. Digital calculating machines already appeared in the seventeenth century, including inventions by Blaise Pascal and Gottfried Wilhelm Leibniz. In 1614, the Scotsman John Napier invented the digital Napier rods, which were used for a long time for multiplication and division. Since the middle of the nineteenth century, mechanical calculating machines have been mass-produced in France. Charles Babbage developed an analytical engine in 1834. A similar machine of the Spanish engineer Leonardo Torres Quevedo in 1920 was also digital, as were the widely used punch card machines from more than 100 years ago.
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