What are the different roles inside a Machine Learning team? What skills are needed for each of them?


  • 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.

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