The Machine Learning Product Manager is someone who works with the Machine Learning team, as well as other business functions and the end-users.
This person designs docs, creates wireframes, comes up with the plan to prioritize and execute Machine Learning projects.
The role is just like a traditional Product Manager, but with a deep knowledge of the Machine Learning development process and mindset.
The DevOps Engineer is someone who deploys and monitors production systems.
This person handles the infrastructure that runs the deployed Machine Learning product.
This role is primarily a software engineering role, which often comes from a standard software engineering pipeline.
The Data Engineer is someone who builds data pipelines, aggregates and collects from data storage, monitors data behavior.
This person works with distributed systems such as Hadoop, Kafka, Airflow.
This person belongs to the software engineering team that works actively with Machine Learning teams.
The Machine Learning Engineer is someone who trains and deploys prediction models.
This person uses tools like TensorFlow and Docker to work with prediction systems running on real data in production.
This person is either an engineer with significant self-teaching OR a science/engineering Ph.D. who works as a traditional software engineer after graduate school.
The Machine Learning Researcher is someone who trains prediction models, but often forward-looking or not production-critical.
This person uses TensorFlow / PyTorch / Jupiter to build models and reports describing their experiments.
This person is a Machine Learning expert who usually has an MS or Ph.D. degree in Computer Science or Statistics or finishes an industrial fellowship program.
The Data Scientist is actually a blanket term used to describe all of the roles above.
In some organizations, this role actually entails answering business questions via analytics.
The role constitutes a wide range of backgrounds from undergraduate to Ph.D. students.