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How Machine Learning Improves Job Data Aggregation For Employment Sites

With hiring volumes increasing globally, employment sites need to gear up to keep databases comprehensive and accurate. Sourcing job data to enrich, cleanse and structure the data has its challenges. Automation is the key. In this article, we discuss how ML improves job data aggregation for employment sites.

Hiring volumes are increasing globally with the UK and the US set for a 58% jump in recruitments, so employment sites are going all out to outperform each other. They are automating data aggregation from across geographies and multiple sources. And they are fully leveraging the benefits of machine learning in data aggregation. AI is now identifying fake, duplicate, and outdated jobs, adding missing fields and enriching data. And cleansing, validation, structuring, and standardization of job data are also automated.

ML is also helping to tag and segment job data and enhance the user search experience. But many job boards are still trying to figure out how to transition to modern tools for job data aggregation. Some are scared to take the technological leap for fear of disturbing operations or risking a temporarily inconsistent brand experience, while others are concerned about security or infrastructure issues. This is causing them to fall behind.

For success in the business of job data aggregation, aggregators are now going omnichannel and consuming countless relevant data points from big data with AI and machine learning models. Mobile sourcing, global recruitment, and social recruiting have become the new norms. And statistics show that 84% of companies are recruiting via social media. So, those stuck to gathering information just from other job boards, advertisements, and company pages today are losing out to modern job portals.

How ML helps in Job Data Aggregation

Manual methods are time-consuming and error-prone, while automation speeds up the process and accuracy. The availability of better and easier tools, faster data-cleaning methods, and the growth of machine learning and AI have transformed data aggregation for job portals.

Automation is the key to listing huge numbers of job openings that also expire at a fast rate, and to keeping job data relevant and comprehensive. But fixed rule-based algorithms used by common crawlers are now insufficient to provide a competitive edge. Investing in AI and ML to source relevant profiles and keep the database updated takes employment sites to the next level. Robust job crawlers, intelligent algorithms, and agile APIs ensure accuracy. And AI and ML add the speed and scale without which trying to navigate big data is impossible.

Using AI for Job Scraping

Modern job data aggregation or intelligent job scraping relies on AI-based data extraction solutions. After support from ML models, AI autonomously learns to gather or scrape job data from across the web. Crawlers extract enormous volumes of job data from multiple sources and save them in the desired format. AI is trained to extract data from pages like career pages of companies, job boards, government sites, social media, forums, blogs, etc. AI also conducts post-collection processing of data enhancing the portal quality and keeping it comprehensive 24/7.

Source Jobs with Value-Added Data

Job data needs enrichment in multiple fields, such as salary, skills, industry, insights on the company, and so on. AI models use NLP to analyze job content and map industry, location, salary, department, etc., and categorize data. Today, job hunters need information on work culture, location, salary structure, employer reviews, the background of the department heads, and peers. Crawlers will extract such data from sources like Glassdoor, LinkedIn, and Ambition Box. Again, extracting relevant data points from different templates often creates trouble which is managed by using machine intelligence. For instance, for compensation, companies use different terminology like ‘CTC’, ‘Salary’, or ‘Remuneration’. The trained machine picks up amounts preceded by currency symbols written next to it as salary.

Job Data Standardization

AI-powered algorithms are used to standardize the data collected from various sources, improving data quality. Data collected from varied sources may have job titles, company names, or skills written in different formats. Some portals may write the title as ‘senior manager’ whole, and some may write it as ‘Sr. Mgr.’ Based on the format required by the target employment site, all the titles are standardized automatically using ML algorithms. Similarly, any other inconsistency is corrected quickly and with no errors. Formatting and standardizing all fields make the data more effectively.

Job Data Cleansing

Companies often post jobs just to increase their database and list jobs for filled positions. To maintain credibility in the market as a reputed job data aggregator, you will need to detect fraud jobs, remove duplicates, and ensure the jobs are current and open. Multi-layered rule-driven validity checks ensure only authentic jobs are uploaded to the database. Such authenticity checks eliminate the chances of posting fraudulent, dead, or duplicate jobs. Intelligent algorithms identify, duplicate, irrelevant, and obsolete job data constantly and keep rectifying. The same jobs posted on multiple job boards are easily detected and removed. Any changes in the job data content are tracked using bots that are further validated and the client’s API is updated. Regular audits with automated tracking and monitor mechanisms ensure data accuracy.


With hiring getting global, job boards are also growing at a fast pace to facilitate the hiring process. Job data aggregators need to source jobs from every nook and corner, extracting all niche and hidden jobs. The data further needs to be checked for accuracy and relevancy. Given the enormous volumes of data, manual methods are not workable. Automated web scraping for jobs from various websites and processing with AI and machine learning modes provides the best solution.

Job data scraping, of course, has its challenges. You need to figure out the sources from which you need to scrape data. Often portals use anti-scraping techniques, making it difficult for you. And then there’s the high cost of using crawlers. You could set up an in-house job-scraping solution, but it will require investment in resources and infrastructure and may not be cost-effective. Hiring a data aggregation company that could partner with you will go a long way in your endeavor to build a comprehensive and accurate job database.

Written By

Chirag Shivalker heads the Content team at Hi-Tech BPO, the company defining and shaping the future of research industry. Their data solutions for consumer and b2b market research and analytics, give a complete view of trends, habits, customer experience, and loyalty for varied products and services.

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