Читать книгу Data Science For Dummies - Lillian Pierson - Страница 35

The roles of data scientist, machine learning engineer, and data engineer are frequently conflated by hiring managers. If you look around at most position descriptions for companies that are hiring, they often mismatch the titles and roles or simply expect applicants to be the Swiss army knife of data skills and be able to do them all.

If you’re hiring someone to help make sense of your data, be sure to define the requirements clearly before writing the position description. Because data scientists must also have subject matter expertise in the particular areas in which they work, this requirement generally precludes data scientists from also having much expertise in data engineering. And, if you hire a data engineer who has data science skills, that person generally won’t have much subject matter expertise outside of the data domain. Be prepared to call in a subject matter expert (SME) to help out.

Because many organizations combine and confuse roles in their data projects, data scientists are sometimes stuck having to learn to do the job of a data engineer — and vice versa. To come up with the highest-quality work product in the least amount of time, hire a data engineer to store, migrate, and process your data; a data scientist to make sense of it for you; and a machine learning engineer to bring your machine learning models into production.

Lastly, keep in mind that data engineer, machine learning engineer, and data scientist are just three small roles within a larger organizational structure. Managers, middle-level employees, and business leaders also play a huge part in the success of any data-driven initiative.