Today’s Data: Life as a Data Scientist

  • Today’s Data: Life as a Data Scientist

    Megan van der Ham

    Unfortunately, this blog is the last blog of the Data Science rubric. For the readers that have become interested in Data Science, today I will discuss the different career possibilities as a Data Scientist. The last months, different topics have been discussed. You were given a general look into what the work of a Data Scientist looks like and the different steps that have to be taken from beginning to the end. In addition, personalization using data and the existing concern of privacy that comes with it, is discussed. This showed the downfall to the digitalization of the world, however, also showed what is done and what you can do to eventually protect your privacy. Lastly, the importance of data visualization when it comes to sharing the information you found during your analyses, is discussed. 

    As you may have noticed, the skill of Data Science is valued in almost every sector, from finance to marketing and from law to consultancy. Therefore, the possibilities are endless and the interests of students are diverse. When interviewing the DSMA Committee, different future plans came to light. Examples are working in FMCG (Fast Moving Consumer Goods), marketing, the creative sector (such as music or television), but also supply chain management, market research and consulting combined with Data Science. Within all these sectors, it can also vary which job you want to pursue, since not all functions have the same relationship with data. It is possible to become a machine learning engineer (also known as data developer), meaning you’re your main task is hardcore coding. As data analyst, your job is a little broader, meaning that you collect, process and perform statistical analysis to answer questions. However, it is also possible to become a data translator, meaning that you are the bridge between the technical teams, made up of data scientists and developers, to translate the findings (often by the means of data visualization) to for example business stakeholders or other departments within the company that have no understanding of Data Science. Often people imagine Data Scientists as people sitting behind their laptop all day creating algorithms, however, this is not the only way to work in Data Science. It is perfectly possible to be a Data Scientist and work with people at the same time. 

    Together with the DSMA Committee, we want to provide you with a list of required skills that are needed to be a good Data Scientists. As seen in the third blog, data visualization and thus data translating, is very important in whatever field you want to work in, since it helps to make all the effort you put into the analyses worth it because it will make others understand it and act according to your findings. Next, losing the overall picture is easy in this workmanship, therefore, it is advised to zoom in to find important details as necessary, but never lose the overall picture, since this is the most important part! Often when applying for a data related job, there are lots of requirements, such as mastering different programming languages and having sufficient knowledge of statistics. In addition, next to the hard skills you learn during your studies or at the job, soft skills are very important as well, think of being convincing, creativity, skepticism and communication skills. This is not taught during your Data Science education path, whether it being a study or separate courses. So keep in mind, when pursuing a career in Data Science, make sure you develop these soft skills too! 

    The first step in becoming a Data Scientist, is having the right education. This could either be a Master’s degree in Data Science, which can be taken at different universities. If this is not an option for you, it is also possible to follow courses for a few months which will get you ready to work as a Data Scientist. However, not all companies are prepared to hire people without a university degree. This means that if you choose to follow this path, expect to start somewhere where Data Science is not the core of the business and gain experience which will eventually help you to get your dream job. 

    Lastly, I want to give you an overview of the type of companies you can work at as a Data Scientist. Lots of companies give you the possibility to experiment and see if you truly like working in this field by providing internships. Great companies in the FMCG are Unilever, Procter & Gamble, Perfetti van Melle and PepsiCo. There are also possibilities for more technical companies, such as IBM, Accenture, Oracle and Alteryx. When preferring the consultancy field look at the well-known companies such as McKinsey, Bain & Company, Boston Consultancy Group. It is also possible to prove your data skills in one of The Big Four accounting firms: Deloitte, E&Y, PwC and KPMG. Lastly, great companies in market research are Nielsen, Statista, SKIM and Ipsos. In addition, it could be interesting to work at startups as well and creating your own Data Science department, since nowadays almost all companies can really use one. Having said that, as last tip from the DSMA Committee: the high salaries might make you warm, however, always look into the company to see if it something for you! 

    As we come to the end of the Data Science rubric, I hope I have informed you well enough and have gotten to let you know the rather ‘mysterious’ field of Data Science. From my (still quite little) experience in this field up until now, I must say that it has surprised me tremendously how big and necessary Data Science is nowadays. It includes so many different purposes, that I think many of you benefit from having knowledge of Data Science. For the people who made it this far, thank you for showing interest in my blogs and I want to wish you good luck in either your Data Science career, or any other career you might pursue!