best-5-tips-data-scientists-can-advance-their-careers

Companies hire data and machine-learning professionals to help them with cutting-edge ML models. They spend often 80% of their time cleaning or dealing with data that is riddled with missing values, outliers, large load times, and a constantly changing schema. It is not uncommon for people to be far from their expectations. Data scientists may initially be enthusiastic to work on advanced models and insights, but this enthusiasm quickly fades amid daily schema changes, tables that stop updating, and other surprises that silently ruin models and dashboards. Although "data science" can be applied to many roles, such as product analytics or putting statistical models into production, there is one thing that is always true: data scientists, ML engineers, and data analysts often sit at the tail of the data pipeline.