With algorithms now used to scan job applications, how do job seekers ensure their application convinces the algorithm? Hint: it's in the CV keywords.
Accountants are increasingly modifying their CVs to give their application the best chance of getting through robot gatekeepers when applying for a role. Experts say to land the job, you need to beat the machine – as well as deliver a strong performance in an interview.
When recruitment firm Hays – which has a strong presence in the accountancy and finance sector – surveyed job seekers, it found 27 per cent of the 6000 respondents had already adapted their CV and online profiles for machine learning, with 54 per cent planning to do the same over the next year.
Machine learning uses an algorithm – a code-based list of instructions for a computer – to solve a problem or make decisions. Algorithms are released in existing datasets to find patterns and extrapolate onto data they haven’t seen before.
The dataset might be HR information a business holds about each employee, how well they have performed, how much revenue they have generated, how well liked they are, how many promotions they have had and social media use.
There are possibly thousands of different data points. An algorithm can be trained to identify the five top traits of a business’s best performers, for instance. It can then be used to identify the same five traits across 10,000 applications. The assumption is that algorithms can make up for certain human cognitive deficiencies during the recruitment process.
“Recruiters can’t look at 10,000 CVs over the course of a week in a rational way,” says University of Sydney Business School associate professor, Uri Gal. “Beyond a certain point they stop reading the whole document for each applicant and look for superficial cues – for instance, where someone went to school. It’s assumed algorithms can help overcome these deficiencies because they are highly efficient, rational and objective.”
However, it’s important to be aware of algorithms’ limitations in the hiring process, Gal emphasises. “Machine learning is extremely alluring because people don’t have to spend thousands of hours sifting through applications. However, when businesses use these algorithms they risk hiring the same people over and over, limiting their ability to be innovative and agile.”
"There's an argument that taking out unconscious bias delivers a more diverse talent pool." Dr Robyn Johns, UTS Business School
There is no easy way to ensure an application makes it through an algorithm and onto a candidate short list, says Hays accountancy and finance regional director, David Cawley. Only applying for relevant roles or for roles for which you have transferable skills is a good start.
“Make sure you add appropriate keywords to your CV and online profile for jobs for which you’re applying,” Cawley says. “If you’re going for a position for which experience in transformation and change is a top requirement, include those keywords throughout your resume so the algorithms pick them up.”
Cawley says it is also important to describe achievements using suitable keywords throughout your CV and online profile, to provide proof of what you’ve achieved.
“It sounds obvious, but make sure your CV is in a format that’s been requested. For instance, we still get applications in really old versions of Microsoft Word that are not supported now. So, make sure the application is sent in a format the technology is going to recognise.”
Also, unless you are a temporary contract worker, don’t job hop too often. “The automation can pick that up – if a criterion for a job is to have held certain roles for a certain amount of time and you can’t meet that, it will immediately recognise it.”
Another tip Cawley offers is to ensure job titles are in a standard format. If you work for a start-up and your title is “chief bean counter”, you may want to change it to chief financial officer or similar when applying for new roles.
Social media profiles are increasingly taken into account by algorithms when assessing applications, and it is particularly important to ensure your LinkedIn profile is complete. Fill in the summary field and maximise it by trying to fit in as many words as possible.
Beat the algorithm
Once people figure out what the algorithm is looking for, they can game the system, Gal says. “You can write your CV or your cover letter to satisfy the criteria for which the algorithm is looking. But different algorithms look for different things and work differently. You have to figure out the algorithm before you can game it.”
While technology is increasingly critical in job applications, the human element in the recruitment process remains important.
“It’s important to optimise your application for the technology, but when you get to the stage where you have human interaction, you need the skills to bring your application to life,” Cawley says.
However, technology is even starting to be used to conduct candidate interviews. Bots are also being used in the interview process to assess responses and body language, says UTS Business School senior lecturer Dr Robyn Johns.
“It takes a lot of the unconscious bias out of the interview process. There’s an argument that taking out unconscious bias delivers a more diverse talent pool.”
Algorithms are here to stay in recruitment. The idea is to tweak your application and online presence so that they match specific criteria for the role. However, keep in mind that sound relationship-building skills will always be important – even if an initial interview is with a robot. At some stage, a human will always become involved in the selection process.
3 tips for beating the bot
- Populate your CV and social media profile, especially LinkedIn, with keywords that match the job description of the role for which you are applying.
- Make sure you use industry-standard job titles such as chief financial officer or payroll officer in applications.
- Don’t switch jobs too often as an algorithm may be trained to only look for candidates who have remained in certain roles for a particular time.
Successful accounting careers need lifelong learning: LinkedIn