ML Engineer/ Data Engineer

• 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 • Supervising the data acquisition process if more data is needed • 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 • Deploying models to production • Finding available datasets online that could be used for training • Defining validation strategies • Defining the preprocessing or feature engineering to be done on a given dataset • Defining data augmentation pipelines • Training models and tuning their hyperparameters • Analyzing the errors of the model and designing strategies to overcome them