A: Data scientists collect, analyze, and interpret data. Usually, before analyzing data, data scientists begin by understanding a problem that needs to be addressed. In business, that problem is usually a process that marketers would like to automate, predict, or understand better. Problems fall into one of two areas: modeling problems and causal inference and experimentation problems.
Modeling problems. To address a modeling problem, data scientists first have to identify the right data, the class of statistical analysis or machine learning techniques they will use, and the metrics they will focus on for optimization. Then, they retrieve the data, clean it up, and summarize it. With this information in hand, data scientists are ready to build models and use them to run experiments. Throughout the experimentation process, data scientists analyze the behavior of the chosen metrics. Finally, they deploy models to address the problem or make predictions about future data. Over time, data scientists will monitor model performance, retrain the models, and test new versions.
Causal inference and experimentation problems. To address a causal inference and experimentation problem, data scientists follow the scientific method. They create hypotheses and design experiments with the data to test their predictions. After running experiments, data scientists analyze the results and form new hypotheses.