ACM CareerNews for Tuesday, September 18, 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 careernews@hq.acm.org

Volume 14, Issue 18, September 18, 2018


LinkedIn Says These Startups Are the Top 10 Most Sought-After of 2018
Inc.com, September 7

LinkedIn recently generated a list of the tech startups in fields ranging from artificial intelligence to blockchain that are generating the most attention from online jobseekers that use the social network. In order to make the list, a startup had to be seven years old or younger, have at least 50 employees, and be privately owned with a U.S.-based headquarters. LinkedIn used four primary criteria to generate the list of startups, including overall interest by jobseekers in the startup, employee growth, and engagement by LinkedIn members with the startup and its employees.

At the top of the list was the ride-sharing startup Lyft, which has raised $4.3 billion to date and is currently valued at $15.1 billion. Another hot startup is Coinbase, which has capitalized on the momentum around Bitcoin. The cryptocurrency exchange and digital wallet startup now has over 20 million accounts. Within the world of AI, the hottest startup is Noodle.ai, which is quickly gaining the attention of tech jobseekers. Noodle.ai is only two years old, but just raised $35 million in June. It uses machine learning and smart algorithms to provide AI-as-a-service to fellow startups as well as established companies.

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10 Highest-Paying IT Job Skills
Information Week, September 12

Learning just one or two new IT skills might boost your earning potential without requiring you to move to an entirely new area of expertise. Not all IT skills, though, are created equal, and it is important to understand which IT skills are in highest demand by employers. For example, the IT Skills and Certification Pay Index from Foote Partners tracks the pay premiums for certified and non-certified skills. In the report from the first quarter of 2018, the 522 non-certified tech skills Foote tracks were particularly valuable. Having just one of these skills increased the compensation of an IT worker by an average of 9.3%. By comparison, the 446 IT certifications Foote tracks boosted salaries by an average of 7.6%.

Within the IT sector, non-certified skills are more important than ever and companies are willing to pay a premium for them. As companies embrace digital transformation, they are finding that they need employees who understand emerging technologies like blockchain, the Internet of Things (IoT), artificial intelligence, machine learning and advanced data analytics. These technologies are so new that few, if any, certifications are available. In addition, digital transformation is driving employers to seek out staff members who can see the big picture and recommend large-scale changes to the way IT operates that could make the company more profitable. They are looking for IT professionals with architecture and business skills who can help guide the company through these difficult transitions.

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Top 5 Must-Have Skills For a Machine Learning Career
Tech Genix, September 10

In order to launch a machine learning career, it is important to start with the basics first. This fundamental knowledge will give you a solid foundation for more complex machine learning projects later. Since machine learning consists of algorithms that are capable of consuming massive amounts of data, it is important to have a solid understanding of data modeling, probability, and statistics. Any domain or academic experience you have in these domains will take you a long way in a machine learning (ML) career. In addition, since there are no dedicated programming languages for machine learning, you should begin to identify languages that have the features necessary for future machine learning projects.

In terms of building the right skills, remember that a lot of machine learning involves work with unstructured data. Its analysis is heavily dependent on data modeling, where the underlying structure of a dataset is estimated to fill gaps. To create robust algorithms, you need robust data modeling knowledge. Machine learning algorithms need to deliver reliable outcomes even while dealing with uncertainty. Thus, the principles of probability and derivative techniques are crucial for data scientists and machine learning programmers. To create models from data, developers need sound knowledge of statistics and its branches such as hypothesis testing and variance analysis. Statistical models are crucial for artificial intelligence algorithms.

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Looking For a Science or Tech Job? Find Out What Top Companies Want
Silicon Republic, September 13

Within the science and technology field, job descriptions are just the starting point for what companies are really looking for in job candidates. In order to understand what makes a perfect candidate, then, it is important to do your own research. Often, top companies are looking for unique qualities or accomplishments that are not even mentioned in a job description. Sometimes these are soft skills, such as the ability to build key relationships. Other times, they are technical skills that cut across two or more functional areas. The article provides an overview of different ways that jobseekers can stand out in a competitive marketplace.

In addition to technical skills and programming ability, leadership potential is an important factor for many companies when considering new candidates for science and tech jobs. Some of the main traits tech employers look for are the ability to build key relationships, a demonstrated ability to deliver exceptional service, and a problem-solving and solutions-focused approach. These skills are important because employers want to empower people to make an impact that matters.

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How Learning Blockchain Skills Can Help You Build Your Career
Entrepreneur.com, August 29

Blockchain technology has unlocked tremendous opportunity for young professionals to significantly advance their career trajectories. According to TechCrunch, demand for blockchain developers is growing at incredible rates, with current demand far outstripping available talent. This means wide-open opportunity for anyone seeking to enter this field. Plus, blockchain is still an emerging technology, so blockchain developers have the chance to start making an impact right away. The blockchain is actively being implemented across a host of applications across multiple industries, opening up many different career paths.

There has been booming demand for blockchain-oriented skills in the job market. The growing rate of global blockchain adoption, combined with the rapidly growing number of use cases, has fuelled an exponential demand for blockchain experts. A recent quarterly report by freelancing platform Upwork revealed that the demand for freelance professionals with blockchain skills registered over 6,000% year-on-year growth in Q1 2018. According to Upwork, the quarter-on-quarter growth for the same skill-set exceeded 2,000% for three consecutive quarters before that. This growth in the demand for blockchain-related skills is not limited to freelance jobs alone. Burning Glass Technologies, which works in the field of job data analytics, has identified more than 5,700 job openings (mostly full time) for blockchain technologists in the previous year. This marks a growth of more than 300% in demand in the last 12 months alone.

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How to Get a Job as an iOS Developer
TechWorld.com, September 10

While iOS developers are in high demand right now, it can sometimes be hard to figure out exactly what skills and experiences employers are looking for. With that in mind, taking an introductory course in iOS is a great way to determine whether you actually want to be an iOS developer and to gain some background knowledge on the subject itself, before taking the plunge. Sites like Udemy and Udacity offer courses for beginners, and you can learn lots of basics from online courses and forums too. Stack Overflow is also a great resource. It houses tutorials, forums and downloadable documentation.

If you are learning the basics of iOS, there are a few things you will want to learn before approaching an app build or job application. It might seem pretty obvious but if you want to be a successful iOS developer then you need to become familiar with Swift. Swift is the Apple programming language and a key skill for anyone wanting to break into this career. Familiarize yourself with the basic framework of Swift, including objects, data types, writing methods and JSON. No one will expect you to be an expert straight away. Companies that offer junior Swift developer roles will most likely offer in-house training from senior developers.

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Is Hiring a Hacker Ever a Good Idea?
ZDNet, September 13

Despite the growing talent gap in the cybersecurity world, many employers are still risk-averse when it comes to hiring hackers to keep them safe from ever-evolving security threats. Yet, say experts, hackers have the mindset to find the gaps in computer networks that cybercriminals misuse, and the right technical skills to help close them. Often these hackers are young, inexperienced and sometimes not even aware they are breaking the law. But how to make sure that the talents of these youngsters are harnessed for good, rather than for evil, is a challenge that the tech industry is still grappling with.

While there are training programs to encourage people to enter the cybersecurity field, some of the traits demonstrated by hackers (and former hackers) cannot be taught in class. Curiosity, tenacity, stubbornness and parallel thinking are more important than any professional certification or computer science degree. In short, being the appropriate type of person for the role is not something you can teach. That is why this question of if you should hire someone with a hacker past is such a tough one because clearly they have the curiosity, tenacity and stubbornness to succeed.

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How to Hire Top Job Candidates Who Do Not Have a 4-Year Degree
Tech Republic, August 27

Companies seeking wider pools of employee talent are increasingly forgoing the application requirement of a four-year degree. Top Silicon Valley companies are among those that no longer mandate a traditional college education for some of their top jobs, according to a recent Glassdoor report. Many fully remote jobs are also available to candidates without an undergraduate degree. Over the past two years or so, having a four-year degree has become less and less of a mandatory hiring requirement for many employers. While you may still see it listed in traditional job descriptions, it is no longer a must-have by any means. This mindset is especially common in the tech industry, where a shortage of skilled talent has led hiring managers to seek candidates who are self-taught or went through a coding bootcamp or other nontraditional educational option.

Nearly 75% of leading tech companies do not require a computer science degree for developers and IT professionals. The growing consensus is that a degree or even certificate only shows they can learn, not necessarily what they know. Another 75% of tech recruiters and hiring managers said they have hired a great job candidate from a non-traditional background. Since more than 70% of developers are at least partially self-taught, vetting candidates only by having a computer science degree means you are missing out on millions of skilled workers. Employers recognize that many qualifications can be acquired in previous jobs, and that experience can sometimes translate into the equivalent of a university degree. Many employers across industries are also aiming to improve diversity recruiting efforts, and by prioritizing potential candidates with the skills and experience to do the job well, but who might not have an advanced degree, this could foster further creativity, learning and productivity within the workplace.

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Computer Science Encyclopedia Can Fill a Gap
Blog @ CACM, September 5

There is an important gap in Computer Science education and professional collaboration that could be filled by a nonprofit online encyclopedia supported by appropriate professionally relevant advertising. This encyclopedia would be managed by a prestigious Editorial Board, which, in turn, would appoint a hierarchy of editors to moderate articles. The Editorial Board could guarantee editorial independence from advertisers, similar to current professional practices for journals and conferences. Anyone would be allowed to register under their own name to submit suggestions and drafts to the editors. And, best of all, access to articles would be free and available to all.

In due course, this CS encyclopedia could support interactive articles with videos, animations, and dynamic narrations. Within a decade, interactive content could be a requirement for almost every article. Over time, the encyclopedia should be organized using ontological services supporting programmatic interfaces for a knowledge graph. Ultimately, the encyclopedia should become a standard, reliable reference that is a trustworthy professionally accountable educational resource for all. Currently, there is no online encyclopedia as described above that can serve as the source of valid scientific references.

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How Did People Write Machine Learning Code in the Past?
Blog @ CACM, September 5

Throughout the past decade, the growing availability of cloud computing power and open-source frameworks have made it much easier to implement machine learning systems than in the past. For instance, popular frameworks have greatly lowered the barriers to writing machine learning (ML) code in languages such as Python. Even more recently, JavaScript-based frameworks have made it possible to experiment with machine learning directly in web browsers. However, ML was not always this accessible to programmers. Before a decade or so ago, it was much harder to get started in experimenting with ML because these convenient software frameworks did not yet exist.

Python started gaining prominence amongst machine learning programmers around the mid- to late-2000s. Although Matlab was still popular, some ex-Matlab programmers moved over to Python during this era due to its growing open-source ecosystem. Java was also still fairly prevalent during this era, along with the popular Weka graphical environment for prototyping ML algorithms. Matlab and Java were the most prominent amongst ML researchers in the early 2000s. Some Matlab programmers wrote their models from scratch, while others purchased custom toolboxes. Regardless of approach used, everyone was forced to understand their own code rather than leaning on frameworks or libraries. Some also relied on C and C++ during this era, especially for performance-sensitive code that ran in production or on embedded devices.

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