fbpx

Why Joblift is making the shift from job search platform to digital career advisor

Joblift uses big data processing on Google Cloud to give job seekers a more personal experience, subsequently helping recruiters find appropriate candidates faster. This is why they’re making the shift from job search platform to digital career advisor.

Jasper Spanjaart on December 02, 2020 Average reading time: 3 min
Share this article:
Why Joblift is making the shift from job search platform to digital career advisor

What will the jobs of the future be like? According to the World Economic Forum, human skills and ingenuity will remain central to the job market. But future jobs such as “digital tailor” (helping customers to make sure clothes ordered online fit perfectly) or “chief trust officer” (managing cryptocurrency transparency) may call for a new kind of career trajectory. Job seekers will need to update their knowledge more often, relying on soft skills and changing career paths throughout their lifetime.

In recent years, the rise of job aggregation platforms that bring together listings has made it easier for recruiters to reach candidates, and vice versa. However, searching through thousands of job postings can be a mighty task, and having to use exact search terms makes it hard to look for jobs that match your skills across categories.

Ready to launch…

That’s why Joblift is launching the next stage of its job aggregation platform, currently operating in the US, the UK, France, Germany, and the Netherlands. “Beyond simply aggregating classified adverts and using full text searches, we want to use data to transform the recruitment experience for both job seekers and recruiters,” explains Denis Bauer. “Job boards haven’t changed for the past decade, and with our Job Coach functionality, we want to simplify the process and offer candidates more value.”

By using machine learning and data-driven approaches, Joblift can also target its recruitment more accurately.

On the candidate side, the vision is to build up a more complete picture of the job seeker’s skills and requirements using an interactive conversational UI and then act as a virtual career advisor in the long term. “For example, after a couple of years, we’ll let the candidate know it’s time to ask for a pay rise, or to look towards the next step,” says Denis. And by using machine learning and data-driven approaches, Joblift can also target its recruitment more accurately. That’s good news for the recruitment agencies and employers looking to fill their vacancies with the best candidate as quickly as possible.

To do that, Joblift needs to constantly analyze the market, making sure it is bidding the right price for the traffic it buys via various marketing channels to sell to its customers. It also needs to cross-reference job seeker data points such as job searches and interactive questionnaire answers, in order to match the right candidate with the right job.

A cloud infrastructure 

As a startup with a small team, Joblift wanted a cloud-native infrastructure that offered managed services to handle operations. It also needed powerful data processing to run its complex relational analytics, but with in-built flexibility to help control costs. To answer these challenges, Google Cloud was its choice from the start. “We are a startup using the latest technology to transform the HR tech space, so for us, the more managed services we can leverage, the better,” says Denis. “It means we can focus on the things that differentiate us, launching in new markets without any significant up-front investments.”

Using managed services to focus on development

Early on, Joblift decided it wanted a containerized infrastructure based around microservices in order to scale easily in the future, and it chose Kubernetes as its container system. That meant it needed a cloud provider that offered managed Kubernetes services, and it felt Google Cloud was the obvious choice. “Kubernetes originated at Google, and was already well supported by Google Cloud at the time,” says Denis.

It built its microservices architecture using Compute Engine instances and Google Kubernetes Engine (GKE) as its orchestration solution, using more than 150 microservices to run its platform, and Cloud Storage for data storage. In its first year, Joblift was able to benefit from funding as part of a Google Cloud program to help startups get off the ground. “As well as sponsorship, we have received lots of support including information on upcoming products and meetings with the product management team,” says Denis. “That’s been super useful.”

“Using managed services improves the speed at which we can innovate.”

Managed services such as Cloud Composer, Cloud SQL, and Cloud Functions help to automate operational tasks and reduce the amount of time Joblift’s team needs to spend on maintaining the infrastructure. “Using managed services improves the speed at which we can innovate,” says Denis. “It means we can focus on the features that help us to stand out from the competition, which is the most important thing from a business perspective.”

Using data analytics to find the right candidate for the job

Another reason why Joblift chose Google Cloud is for its data analytics capabilities. “We chose Google Cloud because our focus is on AI and data, and we believe Google’s products are ahead of its competitors in those areas,” says Denis. “We store more than 60 TB of job posting and job seeker data, and that is growing by 80 GB every day.”

“By analysing both candidate and job posting data, we can gauge the exact demand for delivery drivers in Hamburg or kindergarten teachers in Berlin.”

For its main data analytics warehouse, Joblift uses BigQuery, using all the data on its platform to create reports tracking changes in the market using Google Data Studio. “By analysing both candidate and job posting data, we can gauge the exact demand for delivery drivers in Hamburg or kindergarten teachers in Berlin, for example,” Denis explains. “Those reports steer our sales teams and the whole business side of the company, enabling us to react quickly to changes in the market, and bid dynamically for listings.”

In order to manage its job post inventory data, which feeds into the website, it uses Cloud Bigtable. “Cloud Bigtable is the perfect tool for managing our inventory, because it’s really easy to automatically discard outdated data and keep track of versions,” says Denis. “As it is column-based, adding new categories is simple, so we can easily cross-reference data.”

Welcome to Jobiverse

This data is structured by machine learning classifiers that remove duplicates and arrange the postings according to easy-to-understand categories. “We call this our Jobiverse, the place where we link hundreds of millions of data points,” says Denis. ” It means we can suggest jobs to our candidates with similar attributes to the ones they have been looking at, such as comparable benefits.”

Because it combines data points such as jobs that the candidate has looked at previously with answers about their preferences and personality, Joblift is able to select and prioritize the most relevant results for them.

As for its Job Coach functionalities, Joblift uses AI to choose the right questions to ask candidates, depending on their previous answers. “We use TensorFlow to build our machine learning applications, and on Google Cloud we use Cloud Natural Language.” Because it combines data points such as jobs that the candidate has looked at previously with answers about their preferences and personality, Joblift is able to select and prioritize the most relevant results for them.

 

Share this article:
Jasper Spanjaart

Jasper Spanjaart

Editor-in-Chief and Writer at ToTalent.eu
Editor-in-Chief and writer for European Total Talent Acquisition platform ToTalent.eu.
Watch full profile

Premium partners View all partners

Intelligence Group
Ravecruitment
Recruitment Tech
Werf&

Read the newsletter about total talent acquisition.