Automate machine learning processes such as model retraining
Design techniques for detecting poor model performance e.g. data drift or accuracy degradation
Build highly scaleable systems for deploying and operating models in production
Specify data science best practices to other engineers and embed expert knowledge into the platform

Чем Вы должны обладать

Обязательно

Recommended background: 5+ years of combined Python engineering and machine learning experience
Experience writing maintainable, testable, production-grade Python code
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

Желательно

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.)
Competitive machine learning experience (e.g. Kaggle)