Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Deploying models to production
Qualifications:
Practical understanding of machine learning and deep learning both techniques and frameworks such as TensorFlow, Caffe, Pytorch, Keras, Deeplearning4j, etc.
Identify, build, and deploy algorithms which solve real-world industry problems at massive scale
Programming skill in any language (Python is preferred)
Basic server-side scripting languages understanding (Node.js is preferred)
Experience with SQL and NoSQL databases
Experience with big data tools such as Hadoop, Spark, etc.
Basic knowledge of Distributed Version Control Software (git)
Knowledge with container orchestration such as Docker, Kubernetes, etc.
Knowledge with web services, micro-services, and REST.
Good command of English both writing and speaking.
