Machine learning is typically practiced by data scientists, who help organizations discover hidden value from their data — thereby enabling them to make smarter business decisions. For instance, insurers use machine learning to make accurate predictions on fraudulent claims, rather than relying on traditional analysis or human judgement. This has a significant impact that can result in lower costs and higher revenue for businesses. Data scientists work with various stakeholders in a company, like business users or product owners, to discover problems and gather data that will be used to solve them.
Data scientists collect, process, clean up, and verify the integrity of data. They apply their engineering, modeling, and statistical skills to build end-to-end machine learning systems. They constantly monitor the performance of those systems and make improvements wherever possible. Often, they need to communicate to non-technical audiences — including stakeholders across the company — in a compelling way to highlight the business impact and opportunity. At the end of the day, those stakeholders have to act on and possibly make far-reaching decisions based on the data scientist’s’ findings.
Above all, data scientists need to be creative and avid problem-solvers. Possessing this combination of skills makes them a rare breed — so it’s no wonder they’re highly sought after by companies across many industries, such as health care, retail, manufacturing, and technology.