Internships and MSc. projects at Randstad Groep Nederland

Come join us in Diemen!

About Randstad

Work with impact. At Randstad Groep Nederland IT you keep the country moving, enabling people across sectors to do their work, getting pizza on your table and your suitcase on the plane. Your AI solutions mean tomorrow’s recruiter is smarter and faster but still embodies our human forward approach, combining tech with a personal touch and putting people first – including you. Constantly experimenting, working on new NLP use cases and matching systems or expanding our self-service data platform. If you bring the idea we will provide the freedom to explore, so you can help us shape the world of work. 

Data Science @ RGN

Randstad IT is organized in a variation of the Spotify Engineering Model with squads, tribes, and chapters. Our data science chapter spans 12 data scientists, data engineers and machine learning engineers over 3 departments (IT, finance, and marketing), across 6 different teams. These teams work on recommender systems for algorithmic job matching, natural language processing and information extraction, forecasting, and more. We are further interested in AI fairness and auditing, explainability, and transparency.

Who are you?

We’re looking for students studying AI, data science, or related programs, for either graduation projects or regular internships. Fluency in python is required, and we expect our interns to work autonomously. However, as an intern you’ll be a fully fledged member of our chapter, which means you get to benefit from the knowledge that is being shared in our chapter.

Here’s the overview of our suggested projects:

  • (Deep) Reinforcement Learning-based Planning & Poolmanagement
  • Writing style transfer learning
  • Career pathing MVP
  • Pairwise learning to rank for SmartMatch
  • Revenue forecasting using time-series algorithms
  • Structured information extraction from resumes
  • Salary parsing from vacancies
  • Record linkage for company linking
  • Free text notes and comments for improved job matching
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“Improving automated segmentation of radio shows with audio embeddings”

Update (28/1/2020): Oberon’s thesis was accepted and will be published at the IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), to be held May 4-8 in Barcelona, Spain! The submission is co-authored with Klaus Lux and myself.

Oberon Berlage recently successfully defended his MSc. thesis (title above!) for the Data Science Master at University of Amsterdam, and graduated with a whopping 9!

He’s the first academic offspring of our AI Team @ FD Mediagroep, and worked on BNR SMART Radio‘s segmenter. Oberon improved our text-based segmenter by adding audio embeddings, improving the F1 score with +32%!

His thesis is now online, check it out at: