\u00a0<\/span><\/h3>\nJust a few short years ago, most businesses assigned a single person to handle all data science-related tasks: the data scientist. This data scientist was tasked with overseeing data acquisition, data storage, data cleaning, model training, model tuning, productionization, and more. Many businesses had to experience less-than-ideal outcomes, such as delayed project timelines and subpar model performance, before they realized that they could no longer rely on a single data scientist to handle all of these tasks.<\/span>\u00a0<\/span><\/p>\n\u00a0<\/span>The rise of new AI applications has also demonstrated the ineffectiveness of this generalist approach. Given the complexity of developing AI applications from scratch, many businesses have shifted their focus toward adopting and tinkering with pre-existing models. A greater focus on integration means that, in many cases, engineers are a better fit for these roles than true data scientists.\u00a0<\/span><\/p>\nAs a result, businesses have shifted their hiring focus to more specialized data science roles, including:<\/span>\u00a0<\/span><\/p>\n\n- Data engineers: <\/span><\/b>Data engineers feed the pipeline with massive amounts of historical data and prepare it for analysis. They do this by subjecting data to various quality control measures and storing it in a data warehouse or lake. Without data engineers, data scientists would have to use low quality data to train their models.\u00a0<\/span>\u00a0<\/span><\/li>\n
- Data analysts: <\/span><\/b>Data analysts are typically tasked with reports and visualization of trends and KPIs. These reports serve to check the status quo of the data, or of the business as a whole.<\/span>\u00a0<\/span><\/li>\n
- Machine learning engineer.<\/span><\/b> These engineers are the most recent addition to businesses\u2019 data science teams. Situated somewhere between a data engineer and a data scientist, ML engineers oversee the tuning and productionization of machine learning models.\u00a0<\/span>\u00a0<\/span><\/li>\n
- Data scientists. <\/span><\/b>Data scientists do still play a role in modern data science teams. However, their role is now more confined to the creation and training of machine learning models, and they must rely on collaboration with more engineering-like professional figures.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
A renewed focus on roles outside of the traditional data scientist gives businesses the versatility to harness their data more effectively.\u00a0<\/span>\u00a0<\/span><\/p>\n
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<\/a>The era of the lone data scientist is over.<\/span>![\"Download](\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2020\/02\/BI_BG_SB_800.gif\")
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