Sara Tähtinen is a data scientist with a strong background in mathematics, and a foreground in physics and deep learning. In her work as an expert on numerical simulations to study the production of gravitational waves in the early universe, she has run massive, high-performance calculations on the Supercomputer Sisu at the Finnish IT Center for Science (CSC). Prior to joining DAIN Studios, Sara worked in the research field at the Helsinki Institute of Physics and as a machine learning developer at ZenRobotics.

Sara’s research has extended from the acceleration of the electrons during magnetic reconnection using numerical simulations, tungsten fuzz formation and surface roughing under low-energy helium irradiation, and lattice simulations to study the production of gravitational waves. Sara has extensive knowledge and experience in using Python for Machine Learning (Scikit-learn), Neural Networks and Deep Learning (TensorFlow), as well as SQL, C++, R and FORTRAN and has used various data frameworks and infrastructure, such as AWS.

At DAIN Sara has developed behavioral segmentation models for a fashion clothing brand using CRM and Marketing Automation data.

Sara attended the University of Helsinki and holds a PhD in theoretical particle physics with a research focus on Numerical and analytical investigations of physics beyond the Standard Model, a Master of Science in Theoretical Physics with a research focus on Numerical Modelling of Electron Acceleration in Solar Flares during Magnetic Reconnection, and a Bachelor of Science in material physics with research focusing on Tungsten Fuzz Formation under Low-energy Helium Irradiation. Sara has also given scientific talks around the world at international conferences.

Sara Tahtinen

Sara Tähtinen

Data Scientist
Studio Helsinki

Sara Tähtinen

Data Scientist
DAIN Studio Helsinki

Why do you like working at DAIN Studios Helsinki?

"We have some interesting colleagues that are very active with their free time projects and organising various events. It's a great opportunity to learn more from them!"

If you could solve one problem using data and ML, what would it be?

"I have four very similar looking black and white cats and I would love to have an app for guests visiting my apartment that tells them which is which."