Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines are met
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world

Verifying data quality, and or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Defining validation strategies
Defining the preprocessing or feature engineering to be done on a given dataset
Defining data augmentation pipelines