Our data scientists and engineers love the challenges that their work presents to them on a daily basis and thrive in our agile environment where they can share their knowledge, learn from others, and work together to solve any problems that arise. We are always looking for ways to share the unique problem settings we encounter and to inspire a productive exchange on algorithm development and evaluation. That is why we have partnered with researchers from TU Wien, Politecnico di Milano, and Karlsruhe Institute of Technology to launch the RecSys Challenge 2019, the annual data science challenge of the ACM Recommender Systems conference.
Posts about Data Science
When faced with the challenge to store, retrieve and process small or large amounts of data, structured query languages are typically not far away. These languages serve as a nice abstraction between the goal that is to be achieved and how it is actually done. The list of successful applications of this extra layer is long. MySQL users could switch from MyISAM to InnoDB or use new algorithms like Multi-Range-Read without a change to their application.
Machine Learning (ML) engineering and software development are both fundamentally about writing correct and robust algorithms. In ML engineering we have the extra difficulty of ensuring mathematical correctness and avoiding propagation of round-off errors in the calculations when working with floating-point representations of a number. As such, ML engineering and software development share many challenges… and some of the solutions to these. Concepts like unit testing and continuous integration rapidly found its way into the jargon and the toolset commonly used by data scientist and numerical scientist working on ML engineering.
Tackling hard problems is like going on an adventure. Solving a technical challenge feels like finding a hidden treasure. Want to go treasure hunting with us?View all current job openings