A growing number of people are leaving academic or research positions and joining the illustrious world that is the private sector. This transition can be an eventful one that certainly won’t be the easiest time of your life. The rewards, however, can be great. So, let’s talk about how you can make the move, specifically into Data Science. Below are my tips that should give you a good foundation:
- Understand the area of Data Science that you would like to work in and research it. YES you will need an all-round knowledge of every area but, choose one you wish to specialize in. The Full-stack, unicorn Data Scientist doesn’t really exist. There is probably only a handful in the world, however, the BEST data science teams ALWAYS have guys with varied specialist interests. This part should come easy. RESEARCH the life cycle of a project in that field. This will give you a push in the right direction for the home study that you WILL NEED to do (I’m afraid that part is essential). This may not be what you want to hear, but short term loss is long term gain here.
- Learn the programs and technologies that you will need in the private sector you won’t be using in an academic role – Python for programming (I am not technical and do not wish to get involved in the debate between R and Python, I am going off what clients tell me they want). MATLAB nor PERL is the language for modern problems, get yourself well acquainted with the libraries of Python too and what they are used for. Familiarise yourself with a cloud service and get used to programming within them if possible. Amazon Web Services, Microsoft Azure and Google Cloud Product are the main ones. Learn SQL, there are lots of videos and information online around this.
- Learn how to verbally articulate your thought process will help, A LOT. Selling your thoughts and ideas to others is VITAL in the private sector but remember that not everybody will be technically minded like yourself. Explaining something technically complex to a relative layman is a sign of deep subject understanding and will breed confidence in customers/colleagues from other departments.
- Understand the value that you personally will bring to a private company and show examples of how you could do that. As an example, you could research a recent project in Data Science and give an example of what you could add to that project.
- Job Descriptions always talk about “Passion”. With this in mind, talk about the course that you have undertaken or are undertaking currently.
- Familiarise yourself with a typical Data Science team and understand what each person brings to the project. Get a basic understanding of that job role. A good Data Scientist will be invaluable to a team and other team members will be looking to you for leadership. Remember, you are the MVP.
- YOU must understand Data Engineering. If you do not know if the data has been engineered properly this could be fatal in a project. Managers eyes will be on you to bring a project back from the brink of failure, to do that you must understand what went wrong in the first place, the engineering of the data will be a good place to start.
- BUILD A PERSONAL BRAND! No, you don’t need to be an Instagram influencer, so put the flashy outfit away. A quick trawl through the internet and you will find 100s of Data Scientists who are having problems securing interviews, let alone getting a job.
- Connect with field leaders, comment on their posts but don’t argue. People will always have a different view to you, see this as an opportunity to refine your sales skills.
- Set up a Github account. Get some datasets from the internet (there are thousands for FREE!) and have a play around. Share these on LinkedIn and invite feedback. This will open your eyes to the many different ways to solve a problem – EMBRACE IT!
I spoke with Gopal Karemore – A Principal Data Scientist @ Novo Nordisk, this is what he had to say about his own transition:
Switching from academia to industry wasn’t an easy decision for me at that time but I’m happy I did it. One of the reasons why I joined a company is to have a greater impact on society by developing new medicines for the patients. Data Science in the pharmaceutical industry is different than other fields such as banking or finance where the gap between R&D to production is not huge. The drug development process is neither short nor linear but there are so many factors involved which makes it difficult to directly translate Data science education from academia to the data science capability required in the pharmaceutical industry. Data science with Domain knowledge is a must. When someone from academia with PhD background goes to Industry one of the challenge they have to face is to have the freedom to explore as like any other industries most of the research strategies are based whether or not your research is making an impact to the value of the company.
The number of companies that are aiming to become more data-driven is increasing literally every day. There is, at the moment, a huge skills gap in the space. I couldn’t think of a more exciting career path right now. In my own personal experience, I have found that people within the Data space are super helpful.
For more information about how you can take the first steps then feel free to reach out. As ever, feedback is always greatly received. I hope this has been a help to you.
About the author:
Wesley Cann is an executive search headhunter, specializing in Data across Europe. An experienced team builder, having worked with some of the world’s leading brands all the way down to the most ambitious startups. Feel free to reach out for some free knowledge of how to attract candidates or maybe, a new exciting opportunity for yourself?