Are you an aspiring Data Engineer willing to work on real Machine Learning systems at a large scale and develop your skills with support from experienced developers? If your answer is yes, we invite you to apply for the Summer internship program at Schibsted! This year we are opening our doors to students who want to gain hands-on experience and practise their skills!
What will you do:
You will join us for 2 months (June-July) on a full time basis - the internship is paid.
You will be given your own tasks and senior mentors who will support you through the whole eight weeks of your training.
You will learn about how we apply Machine Learning at scale in Schibsted.
You will be able to use technologies you know in real-life projects and you will also play with technologies which are new for you.
You will work together with experienced developers as well as get to know more about Big Data / Data Engineering / Distributed Computing / Machine Learning.
At the end of the internship period, the candidate will present a summary of what he/she has done, learned, and potential ideas for further development and findings
You would like to know what industrial Data Engineering is for real and are excited to learn how to apply it at a scale.
You are interested in exploring how to use distributed computing for Data Engineering-related tasks.
Our tech stack is AWS / K8S / Spark / Scala / Python / Luigi / Flyte / Tensorflow , so experience with some of those or high interest is a big plus. Some experience with source control tools like git would be helpful as well.
You are a 3rd to 5th-year student.
You have good English skills and are comfortable using it on a daily basis.
You are willing to learn and you have your own ideas which you would like to implement.
To get the most out of your time with us, we expect you to be proactive in your own development. We want to hear your ideas, so be ready to have an opinion and to speak up from day one. Furthermore, you are driven, team-oriented and eager to learn.
About the team:
Team Predict is a central machine learning team within the organisation Data Foundations. Our team’s responsibility is maintaining pipelines that process billions of events for millions of users while keeping users' privacy and data security in mind. We apply Machine Learning and Statistics to produce outputs that power many use-cases in Schibsted such as producing insights about our customers, segments for the online advertising on our sites, and personalization of news.
You will also have the opportunity to learn from other interns across Schibsted. We encourage a diverse, collaborative and creative work environment, where you will develop and learn from others how we build reliable and scalable pipelines. Your work will directly or indirectly impact products and services that many other teams will be using across the whole Schibsted organisation, for a variety of purposes.
The interview process for all our internship roles will start in January 2024.
Summer internship recruitment process:
1. The selected candidates will receive a Home assignment which takes around 3 hours to solve.
2. Going over the provided solution with an engineer from Schibsted - On video call.
3. Interview with a mentor or manager from the team - On video call.
4. If the team picks you, you’ll receive a Job offer.
Schibsted is a family of digital brands with a strong Nordic position, and more than 6,000 employees. Millions of people interact with our companies every day through our leading online marketplaces, world-class media houses. We also help new promising businesses grow. Our joint mission of empowering people in their daily lives is rooted in the values of our media heritage and a legacy of bold change. At our best, we are a fearless force for change in a society built on trust and transparency.
Data & Tech is a central product and tech unit that serves all of Schibsted. We are about 250+ people in Oslo, Stockholm and Krakow, and collaborate closely with other product and tech teams in all units in Schibsted. Areas of responsibilities include data & technology strategy, privacy/data trends/responsible data & machine learning, information security and internal IT.