• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress • Analyzing the ML algorithms and tools 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. • Select appropriate datasets and data representation methods. • Verifying data quality, and/or ensuring it via data cleaning. • Defining the preprocessing or feature engineering to be done on a given dataset. • Defining data augmentation pipelines.  • Training models and tuning their hyper parameters.  • Run machine learning tests and experiments.• Perform statistical analysis for the errors of the model and designing strategies to overcome them. • Deploying models to production. • Extend existing ML libraries and frameworks. 

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