Remember when job postings were in newspapers? We have come a long way since. Job boards have moved the job hunt online. Recruiters no longer have to wait for applications to come in. They can now actively reach out to candidates, and job applications can be completed in days, not weeks.
You even get personalised job recommendations; just input your details into any job board or professional network and you get a list of jobs “you might be interested in”.
So far, so good. But what’s next?
The Pain Points with Today’s Job Recommendations: A Snapshot
Despite the technological leaps in job search, current job recommendations are often lacking. Most job recommendations are only as good as the data they are fed — information about educational qualifications, job roles and work experience.
The problem with this?
Over 70% of the talent pool consists of passive job seekers who rarely update their profiles. The result is outdated profiles, leading to job recommendations that often miss the mark.
Workarounds
Recruiters and job platforms have figured out their workarounds.
Take Amar*, a techie with 7 years of experience in consulting. Despite his interest in Product Management, he kept getting recommendations for consulting roles. It was only after rejecting multiple job recommendations that he received a call to update his job preferences.
Manual intervention from recruiters can be effective (like in the case of Amar) but is difficult to scale.
Some platforms try to solve this by providing a feedback link, or nudging candidates to update their job preferences. But it’s hard to get passive job seekers to engage with these communications.
So, what’s next?
Supercharging Job Recommendations with Machine Learning
Traditional job recommendations are narrow in focus. They consider “explicit” data like designations, skill sets, and educational backgrounds.
In addition to this explicit data, machine learning (ML) powered job recommendations also use “implicit” user behaviour. The new job recommendation model learns user behaviour from profiles similar to the candidate, so it can tailor its job recommendations.
Here are some things it considers: What role is the candidate currently working in? Is the candidate likely to explore new jobs? What job roles are other similar profiles browsing? What jobs have similar profiles applied to? Are they transitioning to a different job role or sector?
The ML-based job recommendation model works in real time. It learns from every click and ignored job posting to refine future recommendations.
For instance, if you’ve been browsing Product Management roles, and others like you have transitioned from consulting to Product Management, the algorithm picks up on this pattern.
Is this a Big Deal?
Absolutely. An ML-based Job Recommendation engine leads to better job applies and expands the recruiters’ talent pool, benefitting both recruiters and job seekers.
The new job recommendation engine will help recruiters reach candidates they wouldn’t have found otherwise, particularly those who haven’t listed the exact skills and work experience a traditional job recommendation engine depends on.
The key metric to measure success here is the “Apply Rate” — the percentage of users applying to jobs they’ve been recommended. The higher the Apply Rate, the more effective the recommendation system.
Relevance is another key guardrail metric for job recommendations. When candidates do not engage with the recommendations, there’s a noticeable dip in the number of job recommendations and the frequency of emails sent to candidates. This ensures that job recommendations are kept as relevant as possible to maintain user engagement.
With the new ML-powered job recommendation engine, we’re seeing better apply rates, and more relevant job recommendations.
Conclusion
AI and Machine Learning are set to revolutionise job recommendations. They promise to make job hunting more intuitive, even for passive job seekers.
The end result?
Recommendations that are not only relevant but also potentially career-defining.
Whether you’re considering moving up the ladder or making a horizontal shift to a new industry, these advanced job recommendations could be the nudge you need to take that strategic career leap.
What’s Next? Don’t Miss Part Two
Intrigued by how machine learning can improve your job search? Stay tuned for part two, where we’ll dive into the technical details of how AI-ML based job recommendation engines are set up.
We’ll explore the algorithms that power these engines, the data science behind the “Apply Rate,” and how user feedback loops continually refine the system for better accuracy and relevance.
Don’t miss it if you’re keen to understand the technology that could define your next career move.
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