p>Typical manufacturing data science work includes detecting process drift, identifying abnormal machine behavior, predicting quality issues, improving equipment health visibility, supporting root cause analysis, and helping teams move from reactive firefighting to proactive detection, triage, and prevention. It includes understanding how the plant operates, identifying where data is generated, defining what "normal" and "abnormal" look like, creating reliable features from machine and process signals, validating model outputs against real-world outcomes, and delivering insights that plant teams can act on.