Do I need a PhD to be a data scientist?+
No — most industry data scientist roles hire on demonstrated skills, not a doctorate. A strong portfolio of deployed models, quantified business impact, and fluency in Python, SQL, and experimentation clears the bar for the majority of postings. A PhD helps for research-heavy or specialized ML roles, but shipped work matters more on the resume.
How is a data scientist resume different from a data analyst resume?+
A data analyst resume centers on SQL, dashboards, and stakeholder reporting; a data scientist resume adds machine learning, model deployment, and experimentation. Lead with models you built, how they reached production, and the revenue or cost they moved. If your work is mostly reporting and SQL, a data analyst resume will read as the better fit.
Should a data scientist resume emphasize models or business impact?+
Both, in that order within each bullet: what you built and how it performed, then the dollars, churn, or engagement it moved. "Built a churn model (AUC 0.88) that preserved $2.3M in annual revenue" pairs technical credibility with impact. Metrics like AUC or RMSE prove rigor; business numbers prove you shipped something that mattered.
How do I show production ML experience with only notebook projects?+
Take one project the last mile: wrap the model in an API, containerize it with Docker, and add basic drift monitoring, then describe that path in the bullet. Even a personal project that is "deployed as a Flask API with monitoring" signals production awareness a notebook alone does not, and it directly matches the MLOps keywords postings screen for.