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Everyone is looking for a data scientist, but for a position in such high demand there\u2019s no clear definition of what the job title describes. I\u2019ve tried to get to the bottom of figuring out what\u2019s involved.<\/em><\/p>\n A company new in the data journey may think they need a data scientist, but they\u2019re probably not there yet. In truth, they probably just need a general data analyst. Companies are calling their roles \u2018data scientist\u2019 but in reality that label is poorly applied, and relates to a lot of different types of roles and specialties.<\/p>\n From a candidate point of view, a true data scientist probably has a PHD. A Masters with lots of experience may be sufficient, but the common thread is stacks of training and knowledge. They may not have a stack of commercial experience, but they\u2019re well-versed in the academic side of their trade, from modeling to statistics and applied mathematics. It tends to be pretty theoretical.<\/p>\n From a client point of view, the role of \u2018data scientist\u2019 can be dependent on how long that business has been on their own data journey. A business early in the process may be looking for easy quick-fix solutions, and someone to lend sophisticated knowledge to the development of your data journey.<\/p>\n But if you get someone in before the data architecture, infrastructure or pipelines are in place, there\u2019s a risk of throwing your money away \u2013 you probably don\u2019t need someone that advanced at that stage. In truth, a business may see some quick wins, but that progress will dry up and someone more junior will suffice.<\/p>\n It\u2019s a matter of putting in the work before a data scientist comes in \u2013 you need clean (or semi-clean) data, and a quality structure process before you can really see or justify the high expense of an expert data scientist.<\/p>\n For a business that is further along in their data journey, they are generally looking for a scientist to come in and use the existing data to predict the future, make large-scale business advisements and help shape the future structure and strategy of the business. That could be in marketing, finance, technology or any other field within the business.<\/p>\n Companies now are moving toward a data-centric business model. Previously data would be compartmentalized, whereas now a centralized data team will communicate and consult to the rest of the business from an internal advisory capacity.<\/p>\n If you had thought you were ready for a data scientist, but this article has made you think otherwise, that\u2019s not to say you don\u2019t need someone with data expertise and knowledge in your business \u2013 it may just be a more junior candidate before you get to that stage in your business.<\/p>\n If you\u2019re still a bit unsure, the easiest way to figure it out will be to reach out for a chat<\/a> \u2013 we place many data scientists (and more junior data roles) each and every month, so our knowledge of the space and our ability to help you find the right person will assist in your journey.<\/p>\n