Senior Data Scientist (#2665)

Ringier Data Technology Department is looking for a Senior Data Scientist to join our team.

Our team leverages the latest models to predict financial figures or the best price for an advertisement, create user facing chatbots and much more applications. We’re also working with cutting-edge technologies, like LLMs, trying to create applications that will drastically increase the visibility of Ringier’s brands.

Requirements:

• Strong record in applying Machine Learning to real-world datasets in a productive environment and are familiar with the main ML frameworks (TensorFlow, Torch, XGBoost, LightGBM, ...).
• Strong experience in Python.
• Experience with LLMs and frameworks like Langchain. You know the best practices and keep following the field so that you use the latest ones.
• Deep knowledge of the latest advances in the NLP domain.
• Experience in software development and confidence in developing the full ML model.
  lifecycle from research to production ensuring aspects such as reproducibility and.scalability (git, dvc, Docker, Amazon Web Services) is a plus.
• (MS, Ph.D.) Degree in computer science, statistics, mathematics or a comparable degree program or equivalent work experience.
• You’re a team player and at the same time can be independent and take responsibility for your tasks. Structured way of working and creativity complete your profile.
• Experience with Palantir Foundry is a plus.
• Full professional proficiency in English.

Responsibilities:

• Working with various datasets from different Ringier companies in different business divisions across publishing and marketplaces and in different regions across continents.
• Be responsible for choosing, improving, and applying various methods from statistics, machine, and deep learning technologies in order to solve business problems.
•  the data of our users, their interests and online behavior as well as the data of the different Ringier’s business units in order to serve the business demands, and enable the future business models.
• Working closely with cross-functional teams to develop prototypes and products for cross-portfolio use cases.
• Communication and visualization of the value of data by describing the findings or the way how techniques work to both technical and non-technical audiences.
• Keeping closely following research and engineering developments mostly in the NLP field, like HuggingFace, LLMs roll in the relevant technologies and methodologies to keep our modern technology landscape up to date.