What is the path to become a Data Scientist

Last fall I was invited to a panel to talk about how to transition from a Digital Analytics to a Data Scientist role. This is a question that I get frequently so here are my thoughts.

  • Do you really want to become a data scientist? I, myself, had doubts for a while until I finally decided that I wanted. There is plenty of room to be “data scientific” in the data analytics role without the hustle of becoming a data scientist. It is a highly competitive market (at least in Toronto), most of the roles do ask for either a master or phD degree (which does requires some money + time investment), and sometimes it might have a slow/small impact in the actual day to day business.
  • What do you want to do as a data scientist? Ok so you decided that it is your dream job and it is something you want to pursue, but how? Well a good start would be to try to learn what you like to be doing as a data scientist. You can start by asking yourself what are the methodologies that you are most comfortable. In Statistics there are many paths, from classification models to prediction, from supervised to unsupervised. Another way of looking at this question is try to understand the problems that attract you more, for example do you like working with marketing? Probably classification and segmentation are more useful in this field. Do you like finance? Attrition and forecasting are more appropriate. This will help you to acquire the knowledge to build your skill set and definitely put you ahead of your competition.
  • What type of company do you want to work for? This is actually a very important question because depending on what you like doing you might want to narrow down your application target. Corporate companies are very different from small companies. In the Corporate world, usually you have data documentation and a more organized infrastructure and majority of the time you join a team with some expertise. When you join a small company you have tons of freedom to test new technologies and techniques as well as have more hands on experience. This will dictate how your resume and application should be built. In a small company, you want to show that you can get things done, building your Github, showing personal projects you worked in the past, coming to an interview with a thought on how you would solve some of the problems presented in the job description. Certifications and degrees might be more relevant in a big company as well as previous experience, majority of the times they already have a team, so what do you bring that adds to their structure is what will set you apart.
  • How do you build your resume? If you have done the last 2 items already, you should know by now that enhancing what you know that is relevant for the role + the job you are applying is super important. I see resumes sometimes with tons of experience but nothing in there can be useful for role. Data Scientists are one of the most competitive roles, make your application relevant by tailoring your resume to what you are applying. A good example is to add you know SAS, R, Python for a role that only needs Python. This way it will make you stand out from other applications.

If you still cannot get a job in the field. I would recommend to connect with a data scientist and understand what is being missed. From not being ready for a coding test, to not being able to explain regular interview questions, there is always room for improvement. And I am sure this will lead to a data scientist role but if not remember data analytics is equally cool and rewarding.

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