ACM CareerNews for Tuesday, September 6, 2022
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Volume 18, Issue 17, September 6, 2022
Tech Job Market Is Up This Year
InfoWorld, August 24
Although several high-profile tech employers have been in the headlines lately for layoffs, the tech job market actually has looked bright for most of 2022. In fact, tech hiring really only began to slip in June, according to a new report. The Dice Tech Job Report for the first half of 2022 found that tech job postings for the first five months of the year grew compared to last year, with June representing the first month-to-month decline in tech jobs since September 2021.
There are several key takeaways from the latest tech jobs report. Demand for tech talent continues to grow, with the number of job postings growing 45% since the beginning of the year and increasing 52% compared to the first half of 2021. Looking ahead to the second half of 2022, employers want technologists with data-related skills. A recent tech salary report found that expertise in data storage and processing tools can translate to superior compensation.
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Meta Is Not the Only Company on the Hunt for Virtual Reality Talent
Dice Insights, August 31
According to a new study, consulting firm Accenture is now the leader in posting VR-related jobs, followed by Meta, Autodesk, Qualcomm, and Apple. The findings are somewhat surprising, given that Meta has been very public in its desire to become the leader in the VR space, especially as it relates to the metaverse. As Accenture quickly expands its services in the metaverse, its focus is slightly different than that of Meta. The VR positions at Accenture have a much higher share in design-focused skills while those at Meta have a greater emphasis on research and product development. Accenture is looking to leverage existing tools to help its clients adapt and operate in the metaverse.
Meta initially led the way with a huge spike of VR-related hiring that peaked in January 2022. However, ever since, its competitors in the tech industry have picked up the pace. Between April and June 2022, hiring for virtual reality roles declined a stunning 81 percent, most likely due to companies tightening budgets in the face of economic uncertainty. Meta may face increased competition from rivals such as Apple, which could be working on its own VR headset for release sometime in 2023. Google and Amazon are reportedly testing their own VR headsets as well, although there is no indication of launching a finished product anytime soon.
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The 10 Most In-Demand IT Jobs In Finance
CIO.com, August 31
The financial services industry has fully embraced new digital technologies, and that is leading to a shift in which skills are most sought-after by employers. Digital transformation appears to be driving demand for tech skills such as AWS and automation, Python for data analytics, Java for developing consumer-facing apps, and SQL for database work. The push is part of an industry-wide trend toward making banking more accessible by giving customers better access to savings accounts, investments, and loans through digital services. Financial services companies need skilled IT professionals to help manage the integration of new and emerging technology, while modernizing legacy finance tech.
Software engineers are one of the most sought-after roles in the U.S. finance industry, with Dice citing a 28% growth in job postings from January to May 2022. The most in-demand skills include DevOps, Java, Python, SQL, NoSQL, React, Google Cloud, Microsoft Azure, and AWS tools, among others. In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Software engineers are at the forefront of digital transformation in the financial services industry by helping companies automate processes, release scalable applications, and keep on top of emerging technology trends. The average salary for a financial software engineer is $116,670 per year, with a reported salary range of $85,000 to $177,000 per year, according to data from Glassdoor.
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4 Things Emotionally Intelligent People Do When They Have a Tough New Job
Fast Company, September 2
Starting a new job can be as exciting as it is stressful. In fact, a majority of people agree that learning the ropes can be the most challenging part of a new job. This includes both learning how to do the work as well as getting to know your new boss and coworkers. The first few months on a job can be nerve-wracking. The good news is that there are four simple things that you can do to navigate through this period most effectively. Emotional intelligence can play an important role here, because it can help to lower your stress levels and help you make the most of your time. With that in mind, the article looks four things emotionally intelligent people do when they face a new job.
Having good self-awareness allows workers to identify and manage the many feelings that they go through when starting a new job. It is important to be aware that experiencing a range of emotions is normal. Every single person at your new company likely had these feelings when they first started working there, and remembering this can help you keep things in proportion. Self-awareness allows us to slow down and take our time to get a better sense of a situation before responding. Also, remember that you do not have to figure out the complex social dynamics of your workplace immediately. Focus on remaining open, friendly, and making a point to go out of your way to introduce yourself to everyone. Making good first impressions will be crucial to long-term success in any new job. Social awareness helps emotionally intelligent people learn to identify the culture and inner workings of an organization, allowing them to more smoothly transition into their new workplace. A good start in making connections is to seek out people who are not in competing roles, but are also new to the organization.
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How to Hire and Retain Gen Z
The Enterprisers Project, August 26
In the post-COVID era, the landscape for tech hiring is changing rapidly, especially when it comes to hiring the new generation of workers who grew up surrounded by technology. For organizations looking to hire new talent, it is critical to have a vision for the IT world of tomorrow. CIOs should work with HR leaders to identify what technical skills are needed to support a modern enterprise, how to manage an increasingly remote workforce, and what members of Generation Z are looking for when it comes to employment. The foundation for future success is having a firm grasp on what the most important technical skills of tomorrow will be.
The remote work model has been successful, and organizations have learned that productivity does not necessarily decrease when managers and their team members are not physically together. This has been a boon for Generation Z, which is a generation that grew up surrounded by technology. Creating an environment that gives IT employees the flexibility to conduct their work remotely has opened the door to a truly global workforce. Digital transformation has leveled the playing field for many companies by changing requirements around where employees need to work. Innovative new technologies, from videoconferencing to IoT, have shifted the focus from location to ability. Because accessing information and managing vast computer networks can be done remotely, the location of workers has become a minor issue.
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Software Developer is the Most Important Tech Job of the Future
Silicon Republic, August 30
In a survey of more than 500 tech workers and employers, 37% said software developer will be the most important technology job in the future. The survey asked more than 500 employers and employees who work in tech for their thoughts on in-demand skills for the future. After software developer, the jobs deemed most important for the future were software engineer, workplace manager, digital workplace program director, head of automation and machine learning engineer.
Tracking the most in-demand skills for tech workers gives a unique perspective on what hiring organizations are looking for now. The top five skills singled out by those surveyed were social media skills; digital marketing; software development; programming, web and app development; and software engineering. The survey asked respondents which digital tools are used within tech industries, based on the ones that are most commonly paid for. Adobe Creative Suite took first place, with 29% of respondents saying it was paid for within their business. Other tools used for communicative purposes, such as Gmail and Slack, were also listed as popular.
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How to Evaluate an IT Job Offer
Information Week, August 31
When evaluating an IT job offer, it is important to examine every aspect of the potential deal, including salary, benefits, and job requirements. Doing independent research into current market rates and speaking with people in your network is important, since economic changes can quickly impact and change what is considered competitive. First and foremost, you need to determine whether the organization can help you meet your goals. Do this at every step of the process so that when you get to the offer stage, there are no surprises.
In order to determine that a job offer meets your goals, review the offer with the recruiter or HR representative you are working with. Step one is to focus on the proposed salary. Next, look for additional monetary benefits, such as the potential for bonuses, long-term incentives, and any stock options. Make sure these items are included in the offer letter. Finally, do not forget to consider non-monetary benefits, including quality of health insurance, paid time off, any wellness reimbursements, and other miscellaneous perks. Check to see if the job title and job description accurately reflect the role you have applied and interviewed for. You will want to be sure the expectations of you are a match for your skillset and level of experience. Additionally, be sure that the offer specifies exactly where you will be working, given that companies continue to adjust their location policies.
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Quiet Quitting Is About Bad Bosses Not Bad Employees
Harvard Business Review, August 31
The act of quiet quitting, or the decision only to do the minimum work necessary to keep a job, is just a new name for an old behavior. For decades, workers have been asking whether their work environment is a place where they want to go the extra mile. And for some time they have been rejecting the notion that work should be the central focus of their lives. Based on analysis of data that has been gathered since 2020, it appears that quiet quitting is usually less about the willingness of an employee to work harder and more creatively, and more about the ability of a manager to build a relationship with their employees where they are not counting the minutes until quitting time.
One way to get a deeper perspective on the quiet quitting phenomenon is to examine recent data on how employees view their managers. Managers should have the ability to balance getting results with a concern for the needs of others. And they should be creating a work environment where people want to go the extra mile. The latest data shows that the least effective managers have three to four times as many people who fall in the quiet quitting category compared to the most effective leaders. These managers had 14% of their direct reports quietly quitting, and only 20% were willing to give extra effort. But those who were rated the highest at balancing results with relationships saw 62% of their direct reports willing to give extra effort, while only 3% were quietly quitting. The conclusion is clear: if a manager makes you feel undervalued and unappreciated, you will be less likely to go the extra mile.
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Computational Thinking in the Era of Data Science
Communications of the ACM, August 2022
Given the importance of data in the modern economy, everyone should acquire data thinking skills in addition to computational thinking skills. Just as computational thinking is currently being acknowledged and taught beyond the scope of computer science education, so should data thinking be taught beyond the scope of data science education. The use of data thinking in the context of computer science education is not only relevant but also vital. The importance of working with real data in computer science education simply can not be overstated. The article explores the fields of computational thinking and data thinking, emphasizing the skills that are most relevant for the workers of today.
While computer science and statistics are a discipline of their own, data science integrates domain knowledge and thinking with computational thinking and statistical thinking. Such a connection between computational thinking and domain thinking can be beneficial not only in the context of data science but also in the context of computer science and in the context of the domain itself. There are two related issues to consider: the potential contribution of computational thinking to domain understanding and the potential contribution of domain knowledge to computational thinking. There are many mutual connections between statistical thinking and computational thinking. Computational thinking with data can improve the understanding of domain knowledge.
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Changing the Nature of AI Research
Communications of the ACM, September 2022
This is a golden era for artificial intelligence (AI) research, with every week seemingly bringing some awe-inspiring feat involving computers that people were sure would be out of reach for quite some time to come. Of particular recent interest are the large learned systems based on transformer architectures that are trained with billions of parameters over massive web-scale multimodal corpora. Prominent examples include large language models that respond to free-form text prompts, and language-image models that can map text prompts to photorealistic images. The emergence of these large learned models is changing the nature of AI research in several fundamental ways.
Since its inception, AI has existed in a gray area between engineering and science. The science part of AI came from its original goals to provide insights into the nature of human intelligence, while the engineering part came from a focus on intelligent function rather than on insights about natural intelligence. This situation is changing rapidly, especially as AI is becoming synonymous with large learned models. Some of these systems are evolving to a point where we not only do not know how the models we trained are able to show specific capabilities, we are very much in the dark even about what capabilities they might have. Often, even their creators are caught off guard by things these systems seem capable of doing. Indeed, probing these systems to get a sense of the scope of their emergent behaviors has become quite a trend in AI research of late.
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