Data scientist, ML engineer, ML platform, applied scientist — what's the difference?
Lesson 1 of 3 in ML Roles & Specializations · 22 minutes
AI/ML titles vary by company. This lesson maps the major role types, what each does, what they pay, and how to choose between them.
What you'll learn
- Distinguish the major AI/ML role types
- Identify the role that fits your background
- Plan the right interview prep for your target role
- Avoid title confusion in your job search
From the lesson
Job titles in AI/ML are inconsistent. 'Data Scientist' at one company is similar to 'ML Engineer' at another. 'Applied Scientist' is FAANG-Canada lingo. 'ML Platform Engineer' is its own thing. This lesson disambiguates so you can apply strategically rather than confused.
1. Data Scientist
The classic role. Built on statistics and analytics; evolved as ML matured.
What they do day-to-day: • Analyze data to answer business questions. • Build models (classification, regression, clustering, recommendation). • Run A/B tests and statistical analyses. • Build dashboards and reports for stakeholders. • Sometimes deploy models to production.
Typical skills: • SQL (heavy use). • Python or R for…