![]() Whether you’re looking to get into the field or you want to switch jobs, it’s really important that you keep an open mind in terms of job title and industry. The exact responsibilities within the field of data science vary a lot, and there are a lot of different roles within the field of data science. Where did it come from, what can we learn from it about the past and how can it guide us in the future? In order to do this successfully, you need to be a business area expert or have contextual knowledge to fit the pieces of the puzzle together and explain to those around you the significance of the data and the insights you have gained from it. ![]() The key responsibility for a data scientist (or whatever your company calls someone who gathers, analyzes, visualizes, or predicts data) is to tell the story of the data. If you work with less technical people, you’ll have to have stellar communication skills, both for writing reports to summarize your analyses as well as for presenting your findings and making recommendations for future action. ![]() Some data scientists are more internal-facing while others have a lot to do with internal, non-technical teams or even clients. You could also be required to develop machine learning models and pipelines or serve your company as a visualization guru. This leads to the sometimes comical variety of responsibilities and titles that can apply to a modern data scientist.Ī data scientist, depending on the company and the specific job, can be responsible for data collection and cleaning. If you’ve been a banker for five years, your odds of getting a data science position in fintech are much better than in healthcare.ĭata science is a relatively new field, and it can be tough for people who aren’t data scientists to explain what data scientists do to laypeople. This kind of contextual knowledge can be developed on the job, but it can be a big advantage if you already have experience working in the industry if you are looking to become a data scientist. If you’re analyzing tree growth data, you should understand the difference between height and height to crown base. ![]() A data scientist needs to be highly skilled in math, statistics, machine learning, visualizations, communication, and algorithm implementation.Īdditionally, a data scientist must thoroughly understand the business applications of their data. From finding areas of the company’s business that could benefit from collection, analysis, and understanding of data to deciding what strategic decisions must be made to improve customer satisfaction or purchase completion rates, a company can ask a lot of data scientists.Ī data scientist is expected to have expert statistical, machine learning, and often economical skills and knowledge. A data scientist has to wear a lot of different hats, and the day-to-day work of a data scientist at Amazon could look significantly different from that of a data scientist at Microsoft. Of all the roles in the tech world, data scientists probably have the highest variation in titles and job responsibilities. ![]()
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