So far, over 3,000 people have applied to one open data science vacancy at a US health tech company this year. The top candidates are given a lengthy and difficult task assessment, which very few pass, says a recruiter at the company, who asked to remain anonymous because they are not authorized to speak publicly. The recruiter says they believe some who did pass may have used artificial intelligence to solve the problem. There was odd wording in some, the recruiter explains, others disclosed using AI, and in one case when the person moved on to the next interview, they couldn't answer questions about the task. "Not only have they wasted their time, but they wasted my time," says the recruiter.

CyberGuy shows you how to manage your online presence. The job search landscape has transformed dramatically in just a few years. Gone are the days when applying for jobs was a part-time endeavor. Nowadays, it's practically a full-time job, especially if you're out of work and have to document your efforts to claim unemployment benefits. The experience can be overwhelming, but fortunately, technology--particularly artificial intelligence (AI)--is here to help streamline the process.

After spending numerous evenings and weekends learning and coding for more than a year, you finally did it! You've now completed your data science program, earned your shiny certificate...now what? Chances are you were looking to get a job in data when you signed up for the course. So let's face this, it is time to get a job! The only thing that's standing between you and success is that first data science job offer.

After spending numerous evenings and weekends learning and coding for more than a year, you finally did it! You've now completed your data science program, earned your shiny certificate...now what? Chances are you were looking to get a job in data when you signed up for the course. So let's face this, it is time to get a job! The only thing that's standing between you and success is that first data science job offer.

The process of job hunting can be arduous. While some sought-after job vacancies are never usually posted on the internet, the ones that are displayed can get lost between company websites, job boards and professional social media avenues. Moreover, sifting through multiple job postings is a cumbersome practice and finding the right position that fits one's passions, experience and goals can be a herculean task for many. The future of job hunting will rely heavily on artificial intelligence (AI), which can simplify the process in more ways than one. While industry strategies may differ, experts in the recruiting field agree that AI can help streamline the connection between employers and candidates. With the vast amounts of data collected on skill-sets, job titles and salaries, AI can help job boards like LinkedIn make accurate predictions on hiring competition and compensation.

According to Nathan Mondragon, finding the right employee is all about looking at the little things. Tens of thousands of little things, as it turns out. Mondragon is the head psychologist at Hirevue, a company that offers software that screens job candidates using algorithms and artificial intelligence (AI). Hirevue's flagship product, used by global giants such as Unilever and Goldman Sachs, asks candidates to answer standard interview questions in front of a camera. Meanwhile its software, like a team of hawk-eyed psychologists hiding behind a mirror, makes note of thousands of barely perceptible changes in posture, facial expression, vocal tone and word choice.

Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of theses networks. That is, the links between users that are generated dynamically intentionally or unintentionally through replies, mentions, and retweets. We propose a probability model of the mentioning behaviour of a social network user, and propose to detect the emergence of a new topic from the anomaly measured through the model. We combine the proposed mention anomaly score with a recently proposed change-point detection technique based on the Sequentially Discounting Normalized Maximum Likelihood (SDNML), or with Kleinberg's burst model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social network posts. We demonstrate our technique in a number of real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as the conventional term-frequency-based approach, and sometimes much earlier when the keyword is ill-defined.