Data Science Intern

About Us

Carv is creating a new category of sports technology by redefining the way skill-based sports are taught with cutting-edge hardware, real-time audio instruction, AI-driven data analytics and content curated by the best instructors in their industries.

Our first product focuses on skiing and was launched in 2016 after a record breaking Kickstarter campaign. Carv’s smart inserts retrofit to any ski boot to measure pressure and motion, data is then processed by your smartphone to deliver turn-by-turn coaching through your headphones and a breakdown of your technique in our app.

The Role

At Carv you will:

  • Help to develop supervised learning/semi-supervised learning models that can detect skiing turn type, surface conditions & skier ability.
  • Use unsupervised learning to explore and understand our 2TB dataset of skiing data.
  • Help to characterise skiing ability into metric form.
  • Plan & execute high quality on-mountain dataset generation.
  • Plan and organise on-mountain testing for our algorithms & models.
  • Help to expose data science findings to the team & the public through dashboards & blogs.
  • Develop reusable data pipelines to keep our datasets up to date.
  • Help to optimise feedback given to our skiers by developing recommendation systems.
  • Help to deploy & monitor models & algorithms in production.


The Necessities

  • Strong knowledge of machine learning and/or statistics.
  • Excellent general purpose programming in python
  • Experience with the classic ML/Data Science python packages: numpy, pandas, sklearn, scipy, etc
  • Experience with time series data
  • Tensorflow or PyTorch experience
  • Skiing experience

The Nice-to-Haves

  • Rust experience

  • C++ experience

  • Ski coaching experience

  • Contributions to the open source community


  • London or Innsbruck


  • UK National Living Wage
  • Option of working from our Innsbruck office
  • Use of our accommodation in Innsbruck


To apply, please send a CV and cover email (no more than 250 words) to