There are those decisions you make and its almost like something changes in the universe, in your whole outlook and attitude to ensure that that decision becomes a reality. It was the beginning of 2017 when I made a hard decision to transition from Academia to Industry. Having been in Academia for several years (since 2010 to be exact), I knew I wanted a change. As much I liked the research work and the projects I worked in, I started feeling like something was missing – the ability to see more the real impact of my work.
With this feeling, I sat down and thought of the kind of work I wanted to do, the experience I wanted to gain, the skills I wanted to use and build on, and more importantly the impact I wanted to make. Looking at those things I knew I wanted a data scientist position, not just crunching numbers, but use data to solve problems that I care about.
At that time, some of the skills I needed in order to perform that job well were not up to par, so I also knew what I needed to do next. I made the decision to use my spare time to learn as much as possible. I made more effort to improve my programming skills, took up data science courses, and started networking more in the circles I wanted to work in. I also narrowed down the types of problems that interested me. I took the opportunity to also gain some business, project management and leadership skills. By beginning of 2018, I was ready, at least ready enough to confidently communicate and demonstrate my abilities.
In a period of 4 months, I went on a total of 13 data science related interviews from 8 companies. Reached 2nd stage with 7 of them, was selected by 3 (not at the same time), and finally landed with one. With this experience, I wanted to share my experiences, just in case they might be beneficial to someone.
Lessons Learned (in no particular order):
– Data science is a hot field, but it is also big and combines various fields. And like me, you will probably feel like there are people out there who can do the job better than you. One can never know everything, but you should show that you are willing to learn and are continuously learning. I took several courses and did small projects that I could point to as evidence. But you should be good at something in this big field. You should have an answer to the “Why you?” question.
– A degree can play in your favor or against. Having a PhD and with only academic experience, I knew going into the interviews that there would be advantages and disadvantages with that. An advantage I observed is that there is a respect that comes with completing the PhD and the fact that you are exposed to new techniques and methods. The disadvantage is that some interviewers might think you are not ready for the fast-paced industry world. Luckily most of the research projects I had worked on were with industry partners, practical in nature, i.e., not just theoretical, and I had done some consulting work that I could point to and show that I can handle the business world.
– It is your practical abilities and attitude that seem to make a big difference. As data science is a practical field, more than the education, its what you can do. Thus many of the interviews included homework tasks that I had to perform and submit. Here I was glad I took the time to improve my programming skills. It also goes without saying that, in this field, as in many IT fields, having a github account or portfolio with examples of projects is essential.
– Find a sweet spot. As mentioned, Data science is big, with companies looking for a variety of skills, some more programming skills than others, some more statistical and mathematical knowledge than others, others more business skills. I found my sweet spot at the center of business and analytic skills, the ability to translate business problems or questions into data science problems, use data science to solve business problems, and the ability to communicate the above effectively.
A few tips to get the interviews:
– Attend events and network – This is a practice emphasized everywhere. But this is something that came handy in my process. Majority of the companies I got to interview with, were the ones where I had sent in an application through a contact I got during data science events. With each company, I sent in an application through the contact and if they had an open position open on their website, I sent in application through there as well. The advantage with the person to person contact is that you also get better feedback on your application than the automatized rejection email versions.
– Following from the above point, don’t just apply for jobs posted on company websites. Read about companies you are interested in or ones that might benefit from the skill you are offering. Identify people from those companies that are mostly likely to take your application seriously, find their emails and send them your application with an attention grabbing email subject header and cover letter. I got one interview through this approach.
A few tips during the interviews:
– Be confident, you also have the deciding power.
– Learn and adapt from each rejection. This is part of the journey. After a rejection, I found it helpful to speak with the interviewer and find out the reasons why they did not select me. This helped me identify my weaknesses and things I needed to improve. But it also informed the jobs I applied to next.
– Take the interviews as learning opportunities. You get to learn a lot about yourself, what kind of work, people and company you want to work for.
That’s all! Would also love to hear your experiences, thus feel free to share.
Also published on Medium.