Senior Applied Scientist, Machine Learning in Epidemiology

We are building a cross-functional, agile team focused on getting things done - solving hard, specific problems for a rapidly growing user base. The ultimate goal is helping people live longer and healthier lives.

We are looking for applied scientists with experience in creating machine learning models for real-life problems, from statistical models (especially related to survival analysis and life expectancy modeling) to deep learning (incl. timeseries).

You would not only help us push the boundaries of state-of-the art in survival modeling, but also shape the direction of the product and making it happen in a form accessible to end users.

You can expect a fast-paced environment and learning a lot every week, so expect challenges related to research in a cross-disciplinary team (statistics experts, medical experts and deep learning experts), learning the domain knowledge related to health and translating the research findings to a practical product.

Minimum qualifications:

  • MS or PhD degree in Computer Science or a related field.
  • 5+ years of experience with Machine Learning (data augmentation, feature engineering, deep learning incl. recurrent and convoluted networks).
  • 3+ scientific publications in related topics.
  • Experience in statistical modeling (especially if related to life expectancy).
  • Proficiency in Python and ML-related frameworks (SciPy, Keras, TensorFlow).
  • Fundamentals in algorithms and data structures.
  • Fluency in English.

Preferred qualifications:

  • Interest in survival analysis / life expectancy modeling using statistical methods and other forms of machine learning.
  • Experience with recommender systems.
  • Experience in the R programming language.
  • Experience in application development in languages such as Dart, Java or C#.

Don’t hesitate to reach out to us to learn more details!