The article discusses various machine learning job roles such as data scientist, machine learning engineer, AI engineer, and research scientist/engineer, offering insights into their responsibilities and required skills.
Data scientists are responsible for running A/B tests, deep-dive analysis, and suggesting improvements to machine learning models, while machine learning engineers focus on building and deploying machine learning models into production systems.
Both data scientists and machine learning engineers require skills in Python, SQL, Git, and basic machine learning concepts, with an added emphasis on cloud systems for data scientists.
AI engineers, a newer role in the field, primarily work with LLM and GenAI tools to adapt and build products, functioning closer to traditional software engineering than the machine learning engineer role.
Research scientists/engineers work on cutting-edge research and expanding machine learning knowledge, with research scientists typically requiring a Ph.D. and research engineers implementing theoretical ideas.
For both research scientist/engineer roles, expertise in Python, SQL, cloud platforms, software engineering fundamentals, and a higher level of machine learning knowledge is essential, with a Ph.D. recommended for research scientists.
The article emphasizes the importance of understanding the distinctions between industry and research roles and advises starting a career with a flexible approach to pivot towards desired roles.
Additionally, the author offers 1:1 coaching calls for career guidance and support to help individuals progress in the machine learning field.
Overall, the article provides a comprehensive overview of various machine learning job roles, highlighting the skills and responsibilities associated with each position in the industry.
The key takeaway is the need for a diverse skill set encompassing technical expertise, software engineering fundamentals, and domain knowledge to excel in machine learning roles.