The role in practice.
WHAT THE WORK ACTUALLY IS
Understanding problems and whether they are suitable Preparing data Building and evaluating models Getting them into production Keeping them working Explaining what they do and do not do
WHAT PROPORTION IS MODEL BUILDING
Small, and smaller as systems mature.
WHAT DISTINGUISHES THE ROLE FROM DATA SCIENCE
Responsibility for production systems, not analysis.
WHAT SKILLS MATTER
Software engineering, substantially Data handling Machine learning fundamentals Systems and infrastructure understanding
WHAT MATTERS BEYOND TECHNICAL SKILL
Saying when a problem is not suitable Explaining limitations honestly Resisting pressure to deploy something unready
WHY THAT LAST POINT
A model deployed before it works damages confidence in everything after it.
WHAT TO BUILD A REPUTATION ON
Systems that keep working, and honest assessment.
WHAT TO LEARN CONTINUOUSLY
The field moves, but fundamentals do not.
WHAT TO BUILD FOR A PORTFOLIO
An end-to-end system: data, model, deployment, monitoring.
WHY NOT A NOTEBOOK
Everyone has notebooks. Few have working systems.
WHAT TO AVOID
Chasing techniques rather than solving problems.
