Who does what.
WHAT A DATA SCIENTIST TYPICALLY DOES
Analysis, experimentation and modelling, frequently with a research emphasis.
WHAT A MACHINE LEARNING ENGINEER TYPICALLY DOES
Builds and operates production systems containing models.
WHAT AN ANALYTICS ENGINEER DOES
Models data for analysis, without machine learning.
WHAT A DATA ENGINEER DOES
Builds the data infrastructure everything depends on.
WHAT A RESEARCH SCIENTIST DOES
Develops new methods, usually requiring an advanced degree.
WHAT AN ML PLATFORM ENGINEER DOES
Builds the infrastructure other practitioners use.
WHAT THE TITLES ACTUALLY MEAN
Very little consistently. The same title covers different work between organisations.
WHAT TO READ INSTEAD
The described responsibilities.
WHAT DISTINGUISHES SENIORITY
Judgement about what to build, and what not to Ability to deliver end to end Communication with non-specialists
WHAT MOST ORGANISATIONS ACTUALLY NEED
Someone who can take a problem from framing to production and keep it working.
WHAT THAT MEANS FOR YOUR LEARNING
Breadth across the lifecycle matters more than depth in modelling.
WHAT OPENS MOST DOORS
Software engineering ability, alongside machine learning knowledge.
