Guest Post: AI Will Dominate Recruiting – So Prepare For Major Changes In These Areas

AI Will Dominate Recruiting – So Prepare For Major Changes In These Areas

Most recruiters are busy with their day-to-day work. So, some fail to realize that many recruiting processes and tools currently in use will soon improve significantly by the continual learning provided by Artificial Intelligence (AI). In addition, not only will AI and its advanced cousin Machine Learning (ML) make recruiting processes faster and cheaper, soon and in many cases are already adding significant new capabilities that were simply not possible with legacy systems. However, relax, this isn’t a job security issue, it’s an opportunity to improve performance with little effort on the recruiter’s part.

It’s quite common these days for the CEO’s from Amazon, Google, MS, Facebook and Apple to expound on how artificial intelligence and machine learning will dominate their businesses over the next few years. Even Vladimir Putin stated, “The country that leads in artificial intelligence will lead the world.” It’s also important to realize that in addition to contributing to the most visible product areas, like digital assistants and driverless cars, “Machine learning and AI are a horizontal enabling layer” says, Jeff Bezos of Amazon, meaning that AI will impact and improve every major function and its processes and decisions. Recruiting leaders shouldn’t be surprised that I predict that “machine learning will soon begin to dominate every major aspect of recruiting.” Just as previous technologies like ATS’s and CRM’s have already transformed recruiting. It’s important for recruiters to be aware that there is an upcoming wave of mostly vendor developed recruiting applications that assist in producing extraordinary hiring results because they include machine learning capabilities.

The goal of this article is to highlight the upcoming AI/ML and technology changes that are likely to occur in each of the major areas of recruiting.

 

The Top 15 Recruiting Areas That Will Be Most Impacted By AI And Machine Learning

The areas of skills-based recruiting and job/candidate matching that will be impacted are below. Note that they are listed so that the initial items in the recruiting process appear first.

Recruiting areas related to finding and attracting prospects

  • Advertising placement and content – Machine learning will continually improve your placement process for branding materials and job postings rather than relying on costly trial and error approach to advertising. This is critical because accurate placement is essential if you expect to get the right kind and number of applicants. Systems will continually learn by analyzing visitor cookies and response rates so that you place your highly targeted materials in front of the right people at the right time. Also, machine learning technology can help you continually refine your content so that it gets the highest response from your recruiting targets.
  • Your own website and social media – continually improve by firms using machine learning on their web and social media pages to better attract and continually engage your target audience. Software bolstered with machine learning will also be able to monitor and make you aware of both positive and negative comments that others make about your firm and jobs on the Internet and social media.
  • Finding individual prospects – during sourcing will become much more automated and accurate when augmented with machine learning capabilities. Automated sourcing programs will be able to find many more and better matches, based on the continually updated target profile that you develop as a result of feedback. There are already vendor packages that allow you to identify currently employed individuals (e., passives) that are likely to quit soon and prospects that are likely to be diverse.
  • Enhancing prospect profiles – can make the existing candidate profiles found on sites (like LinkedIn) more complete by supplementing them with additional information that a machine learning program will find on the Internet. Machine learning driven programs can sort through a prospects search histories, cookies and social media sharing. The additional information on a prospects interest, capabilities and behaviors might indicate that a candidate can do things that they haven’t done in the past. Once they apply, chatbots can contact an applicant directly to clarify unclear elements in their resume or profile.
  • Improving job descriptions and postings Recent research data has revealed that job descriptions and job postings can be dramatically improved so that the content better attracts your target audience. So, rewriting them can reduce terms that create a bias. Software can now help you reduce those biases and add content that draws initial attention and that attracts more qualified applicants.
  • Responding to questions – from potential or actual applicants is immensely time-consuming for recruiters. So many firms are already utilizing chatbot’s to answer questions quickly 24/7. The U.S. Army, for example, has been using its Sgt. Star chatbot for over ten years to answer its extremely high volume of questions. Chatbots can also periodically update a candidate status, once again saving recruiters time.
  • Personalize selling – Machine learning uses big data to identify the attraction factors and the elements of the firm’s employee value proposition that best engage certain personas (e., types of individuals). Rather than a “one-size-fits-all” approach, this allows you to make your attraction, marketing messages and personal communications more effective because they are highly personalized to the individual.

Recruiting areas after candidates apply

  • Resume sorting – with machine learning software uses the resumes of successful hires at your firm to find patterns and then it can use these past success patterns as a basis for predicting which resumes and candidates are most likely also to be successful when hired. If programmed correctly, resume sorting software can also help to eliminate a great deal of unconscious bias in resume screening and candidate slate selection. Machine learning assisted search programs can also help you find hidden or lost talent within your ATS database.
  • Matching people and jobs – Using matching programs supplemented by machine learning can help a firm determine if there are any, less obvious, jobs that an applicant would also qualify. Matching people with jobs will also be improved by looking not just at an applicant’s past job titles and degrees, but also at their skills and capabilities.
  • Interview scheduling – is time-consuming and dramatically reduces your speed of hiring. Fortunately, there is existing software that allows a candidate to self-schedule their own interviews depending on their availability.
  • Interviews – can be time-consuming, so it makes sense to automate the initial ones with a chatbot that provides personalized questions based on your job profile. Also, there already exists technology that allows the use of neuroscience tools like voice and facial recognition to assess aspects of video recorded interviews that no humans could detect. There are even voice modulation programs that can help you obscure the voice of telephone interviewees so that it’s harder to identify their gender and national origin.
  • Supplemental candidate assessment – in addition to traditional interviews. Natural language processing can check language skills and online technical tests and challenges can help to assess the skills of applicants. There are automated programs that can more consistently determine cultural fit. Eventually, virtual reality simulations will be able to supplement interviews by giving candidates actual problems from the job to solve.
  • Offer acceptance – based on the candidate’s persona and profile. Recruiters can put together offers that are more likely to be accepted while at the same time treating all genders equally when it comes to compensation.
  • Learning from hiring failures – By definition, machine learning processes continually identify mistakes and errors. Recruiting will have an ongoing failure analysis process that continually and automatically finds hiring and bias errors and their root causes, allowing recruiting processes to improve at a much faster rate.
  • Other technologies – in addition to AI/ML technologies. Block Chain may eventually make checking educational and employment credentials easier and more accurate. Skype and video technologies already make it much easier to interview remote candidates without requiring them to travel. Machine learning will make predictive analytics in the area of projecting the future trajectory of finalists (in the areas of performance, retention and promotions) much more accurate.

Final Thoughts

Although most firms don’t track it, the average failure rate of new-hires at all job levels hovers around 50%. For example, Leadership IQ found that when “they tracked 20,000 new hires, 46% of them failed within 18 months”. Former Harvard Professor and author Michael Watkins reveals that “58% of the highest-priority hires, new executives hired from the outside, failing in their new position within 18 months”. Part of this broad failure results from overworked recruiters, normal human errors and unconscious biases throughout the recruiting process. Fortunately, the machine learning technologies highlighted above will soon minimize those problems through automation and continuous improvement. The results will be hiring faster, lower cost and more importantly hires that perform better on the job (i.e., quality of hire), that are more diverse and with fewer hiring failures. Recruiters should also take note that as more recruiting transactions are automated, it will allow current recruiters to “raise the bar” and to move into the more strategic Talent Advisor role.

Finally, recruiters should also be aware that they will soon be recruiting many more individuals into machine learning roles. The share of jobs requiring AI skills has grown 4.5 times since 2013 (Source: Stanford).

Want to see how machine learning can help you find better technical matches for your open roles?  Check out CodeSignal Recruiter or attend an upcoming webinar.

About the Author:

Dr. John Sullivan is an internationally known HR thought-leader from the Silicon Valley. Specializing in strategic Talent Management solution. He is a prolific author with over 900 articles and 10 books covering all areas of Talent Management. Fast Company called him the “Michael Jordan of Hiring”, Staffing.org called him “the father of HR metrics” and SHRM called him “One of the industries most respected strategists”. He was selected among HR’s “Top 10 Leading Thinkers” and was ranked #8 among the top 25 online influencers in Talent Management. Dr. Sullivan is currently a Professor of Management at San Francisco State

If this article stimulated your thinking and provided you with an accurate picture of the future of technology in recruiting, please take a minute to follow or connect with Dr. Sullivan on LinkedIn.

© Dr. John Sullivan 5/2/18 for Codefights

Interview Practice: Graphs, Advanced Trees & RegEx

New Interview Practice Topics: Graphs, Advanced Trees, RegEx

We’ve just added three-brand new computer science topics to Interview Practice! Get ready to dive deep on Graphs, Trees: Advanced, and RegEx. We’ve added these topics to our Extra Credit learning plan, which covers all of the topics in Interview Practice. 

Why are these topics so important to know for technical interviews? Read on for a brief introduction to each concept!

Graphs

Graphs Interview Practice TopicA graph is an abstract data structure composed of nodes and the edges between nodes. Graphs are a useful way of demonstrating the relationship between different objects. For instance, did you know that you can represent social networks as graphs? Or that the 6 Degrees of Kevin Bacon game can be modeled as a graph problem? Graph questions are really common in technical interviews. In some cases, the question will be explicitly about graphs, but in other cases the connection is more subtle. Read our tutorial to get up to speed on this topic and to learn how to identify this kind of question. Then practice your skills on graph questions from real technical interviews!

Trees: Advanced

Trees: Advanced Interview Practice TopicA tree is a data structure composed of parent and child nodes. Each node contains a value and a list of references to its child nodes. Tree traversal and tree implementation problems come up a lot in technical interviews. Common use-cases for an interview are: needing to store and do searches on data that is sorted in some way; needing to manage objects that are grouped by an attribute (think computer file systems); or implementing a search strategy like backtracking. You need to be very familiar with how to deal with these kinds of questions! (The tasks in Trees: Advanced ramp up in difficulty from the ones you get in the Trees: Basic category, so make sure you finish those questions before moving on to these ones!)

RegEx

RegEx Interview Practice TopicA regex is a string that encodes a pattern to be searched for (and perhaps replaced). They let you find patterns of text, validate data inputs, and do text replacement operations. A well-written regex can make it easier to solve really tricky interview questions like “Find all of the 10-digit phone numbers in a block of text, where the area code might or might not be surrounded by parentheses and there might or might not be either a space or a dash between the first and second number groupings”. While the specifics of how to implement a regex can vary between languages, the basics are pretty much the same. In the topic tutorial, we cover regex character classes, quantifiers, anchors, and modifiers and how to use them to write a good regex.    

Start now

These topics might not get asked in every interview, but they’re important to know! Read the tutorials about each concept, then solve the real interview tasks to practice your skills and solidify your understanding of the topic. (Learn more about how we’ve updated the Interview Practice experience to make it an even better practice and preparation tool.)

If you’re signed up for the Extra Credit learning plan, these topics have been added to your Interview Practice page already. If you’re signed up for a different learning plan, you can switch over to Extra Credit. Or you can sign up for a customizable Freestyle plan and add these topics!

Intro to Hash Tables

Hash tables are a must-know data structure for technical interviews. They’re used to store unordered collections of (key, value)pairs, in which the key must be unique. Item lookup by key, inserting a new item, or deleting an item are all fast operations – approximately O(1). Because they give you quick and cheap insertion, deletion, and lookups, you’ll be able to use them to solve many different types of interview questions.

When should you use a hash table?

Some common interview questions in which you should probably use a hash table are:

  • When there’s a unique, non-arbitrary identifier that you can use as a key for lookup. (Example: A caller ID function that uses a person’s phone number to retrieve their information.)
  • You need to quickly determine whether an element belongs to a collection. In these cases, you can represent the collection as a hash table with the elements as keys. (Example: A Scrabble checker that determines if a word is valid or not.)
  • You’re solving a problem about invariants. (Example: Checking whether a word has an anagram.)

To get a good basic introduction (or reintroduction) to hash tables and how you can use them, watch our Intro to Hash Tables tutorial video!

(For even more information about hash tables, check out the Hash Tables section in any of our Interview Practice learning plans.)

Practice makes perfect!

When you feel ready to practice on some real interview questions about this data structure, head to Interview Practice on CodeSignal. Sign up for one of our technical interview learning plans. (No matter which one you choose, you’ll have access to the hash tables section.) You’ll learn more about them in our hash tables topic overview, and you’ll solve real interview questions about them. All that practice will get you good at recognizing when to use them and how to implement them during technical interviews!

Ace Your Next Interview With CodeSignal

Ace your next technical interview!

You know that you need to prepare for technical interviews – right? Of course you do! Companies rely on technical interviews to weed out people who can’t cut it. And for qualified candidates, they’re used to gauge aptitude, interest, and intelligence. So the stakes are really high, and you need to do everything you can to give yourself a competitive edge.

But it can be hard to know what exactly to study. There’s so much information out there, and sometimes it feels like you have to know everything. If you start studying without a plan, you risk wasting your time by preparing for the wrong sorts of questions, not studying enough, or any other issue that might hamper your ability to knock the interviewer’s socks off. And we want those interviewer’s socks to be knocked all the way off! In other words, we want to make sure that you ace your next technical interview.

That’s why we’ve made some changes to Interview Practice that make it an even better interview prep tool. Now you can join a study plan that gives you a timeline and a way to track your progress (or you can create your own plan). Each study plan has essential topic overviews and real interview questions from real companies. This makes it easy to know what topics to cover and how much to study.

Choose a study plan

Each study plan covers the most commonly-asked interview topics that you should prepare for, given the amount of time before your interview.

  • Crash Course: As the name implies, the Crash Course is perfect for when you have a technical interview coming up soon! This study plan will get you up to speed on computer science fundamentals. These are common topics that you must know in order to do well on interviews. Choose this plan if you have two weeks (or less) to prepare for an interview.
  • Fundamentals: If you have an interview coming up in the next 2-6 weeks, this is the perfect study plan for you. Since you’ve got a little more time to prepare, the Fundamentals plan covers some other common topics like bits, strings, sorting algorithms, and useful problem-solving techniques.
  • Extra Credit: If you plan to start interviewing at some point in the future, but don’t have any specific plans yet, check out the Extra Credit study plan. Extra Credit covers big topics like number theory, counting, and geometry. These concepts are really important but don’t get asked as often in interviews.
  • Freestyle: If none of these options is exactly what you need, don’t worry. It’s quick and easy to create a Freestyle plan! Just select the topics that you need to study and the amount of time you have before your interview, and our system will create a customized study plan for you to follow.

Once you’ve chosen a study plan, Interview Practice keeps you on track by recommending a minimum number of coding tasks to solve each day – and reminding you to start hustling if you’re not meeting your daily quota!

Study important concepts

A mainstay of the technical interview process is asking questions that help the interviewer determine how well you understand computer science fundamentals like data structures and algorithms. The interviewer is also assessing whether you can implement these concepts appropriately, and whether you take time and space complexity into account. Depending on where you are in your career, some of these topics might be ones that you haven’t touched since you were in school. Or maybe you’ve never formally studied them.

Interview Practice gives you a refresher course on these common interview topics. You need to be strong on these in order to do well in technical interviews, whether you’re a junior developer or have been in the field for years.

Study important interview concepts.

Solve actual coding problems

The questions you get asked in technical interviews are aimed at making sure that you have a fundamental understanding of how to write code. The interviewer also wants to make sure that you take edge cases and optimization into account. While you’re whiteboarding, interviewers are also gauging how you think while you’re working through a problem.

Actually writing code that solves the actual technical interview questions makes you more comfortable with the process. We can’t emphasize this enough. The absolute best way to ensure that you’re good at interviewing is to practice solving coding interview problems!

Each topic in Interview Practice has real interview questions from real companies for you to practice on, ordered by difficulty. As you solve them, you’re solidifying your knowledge of the topic and becoming familiar with this kind of problem.

Get going!

Interview Practice gives you a plan and helps you stick to it. This eliminates the guesswork about what you need to study,  how much you needs to study, and how you should organize your time. Work your way through each topic, reviewing each concept in the overview and solving the questions. As you practice, you’re becoming a better programmer. You’re also giving yourself a serious advantage in technical interviews!

Time to get started! Head to CodeSignal and get started on the new and improved Interview Practice today.