Your career starts on Magnet.me
Create a profile and receive smart job recommendations based on your liked jobs.
In the contemporary job market, efficient talent acquisition relies heavily on precise matching of job seekers with relevant job opportunities. One crucial aspect of this process is the accurate analysis of job titles, as they play a pivotal role in conveying the nature and requirements of a position. This thesis proposes the design and implementation of a specialized stemmer for job titles in Dutch, with the aim of enhancing semantic matching capabilities within the recruitment platform at Magnet.me.
The primary objective of this thesis is to create a robust and context-aware stemmer tailored to the unique linguistic nuances of job titles in the Dutch job market. By developing an algorithm that intelligently identifies and processes variations of job titles, we aim to improve the accuracy of job classification at Magnet.me. The stemmer will serve as a vital component in classifying jobs, which in turn is a key component of our search functionality and recommendation systems.
The research will adopt a mixed-methods approach, combining qualitative analysis of Dutch job titles, linguistic patterns, and user feedback with quantitative assessments of the stemmer's performance. Natural Language Processing (NLP) techniques and machine learning algorithms will be explored to develop a context-aware stemmer that adapts to the dynamic nature of job titles.
The successful implementation of a Dutch stemmer for job titles holds immense significance for Magnet.me, as it directly contributes to the platform's mission of facilitating meaningful connections between job seekers and employers. The improved semantic matching will enhance user experience, improve recommendation quality, reduce search friction, and ultimately foster more efficient and accurate job placements.
This thesis seeks to bridge the gap between language processing technology and the intricacies of Dutch job titles, offering a tailored solution to optimize semantic matching within the Magnet.me platform. The outcomes of this research have the potential to significantly elevate the effectiveness of talent acquisition processes, benefiting both job seekers and employers.
Magnet.me is the #1 career network for students, graduates and employers in the Netherlands.
Magnet.me uses ML to empower every student to build a network of interested employers and to discover jobs based on their profile and preferences. More than 300,000 students are on the network to kickstart their careers.
On the employer side, 5,000+ companies - from multinationals like Unilever, Heineken and McKinsey to startups and scale ups - use Magnet.me to establish meaningful connections with young talent and to fill their internships and jobs without hassle.
We are a small team of ~25 Magneteers. You will be part of the Product Team, where we develop our own platform; including our Machine Learning solutions. You will be supervised by Alex Walterbos, MSc (TUDelft) and Rogier Slag MSc (TUDelft).
Apply below to get in touch!
Magnet.me is the #1 career network for students, graduates and employers in the Netherlands. Magnet.me uses AI to empower every student to build a network of interested employers and to discover jobs based on their profile and preferences. Companies use Magnet.me to establish meaningful connections with young talent and to fill their internships and jobs without hassle. More than 300,000 students and 5,000 companies, from multinationals to startups, are currently on the network.
I really like that everyone at Magnet.me is very open and works hard towards a common ambitious goal. As a Data Analyst, I monitor our marketplace to provide insightful data for informed decisions. This role challenges me both strategically and technically, which stimulates me to keep on learning!
What I love about Magnet.me, is its versatility. We have a great mix of creative marketeers, a helpful support squad, typical Delft developers, and customer-oriented sales people. Everyone adds something, especially to the great atmosphere during Friday afternoon drinks.