Understanding of different machine learning algorithm families and their tradeoffs (linear, tree-based, kernel-based, neural networks, unsupervised algorithms, etc.)
Good command of scientific Python toolkit (numpy, scipy, pandas, scikit-learn)
Understanding of time, RAM, and I/O scalability aspects of data science applications (e.g. CPU and GPU acceleration, operations on sparse arrays, model serialization and caching)
Software design and peer code review skills
Experience with automated testing and test-driven development in Python
Experience with Git + GitHub
Comfortable with Linux-based operating systems
Nice-to-have
Previous experience of deploying and maintaining machine learning models in production
Experience with Natural Language Processing
Experience with Computer Vision
Experience with deep learning libraries and frameworks (TensorFlow, Keras, PyTorch etc.)