An ML Engineer and a Data Scientist are both working on a fraud detection system at a payments company.
(1) Describe what each person owns end-to-end on this project.
(2) What are the success metrics each role optimizes for, and how do those metrics differ?
(3) What would happen to the fraud detection system over the first 12 months in production if the ML Engineer role did not exist? Be specific about the failure modes.
(4) Give one concrete artefact each role would produce — for the data scientist, what does their handoff look like; for the ML engineer, what does their first month of work produce?