Often Missing SkillsExperiment design and causal inferenceML model evaluation and trade-off analysisData platform architecture basics (feature stores, pipelines)Product discovery for data/ML use casesData privacy and responsible AI practices
Development SuggestionsComplete an end-to-end ML product capstone (problem framing → model → A/B test) and publish results; earn a recognized product or ML credential (e.g., Pragmatic PMC or AWS ML Specialty) and apply learning on a real internal pilot.