By Renato Boemer, Renato Boemer
Photo by Sam Dan Truong on Unsplash
So, you have been studying Data Science for some time and you are now looking forward to the next step: land your first job as a Data Scientist. However, if this is not your first job ever then probably this is the first time applying for a role unrelated to your previous career. So, why not learn from other people’s mistakes?
In my post about changing career to Data Science, I started with online learning at DataQuest. Then, earlier this year, I made one of the best decisions of my career: I have signed up for the Le Wagon bootcamp — I also wrote a post about it. Although bootcamps are intensive in their nature, the toughest part of any career change is to land your “first gig.”
Recently, I have joined a company called Nextdoor as a Data Scientist based in London, UK. But the process of finding my first job as a Data Scientist was by no means easy. I have applied to over fifty roles, done several interviews, some of which were purely technical or included live coding. During this period, I have learned a lot and would like to share five tips that could help you find your first job as a Data Scientist:
It seems obvious but unfortunately, it’s not easy to recognise what you don’t know. What is worse, you may think you know, but you don’t. Let me give you an example: during the bootcamp, I have created several machine learning models using Scikit-learn’s logistic regression. I was tuning the penalty parameter almost intuitively, especially between
l2, which refer to Lasso and Ridge respectively. So far so good.
In my first interview, I decided to throw in those concepts to show some knowledge but it backfired. As I tried to explain the difference, I realised that I knew how to apply them, but I didn’t understand the concept (let alone the math) behind it. Needless to say that I didn’t get that job. My advice here is to deep dive into few projects to the point that you know your code line by line. Try explaining to other colleagues, in mock interviews, why you have chosen each model, parameter. You will notice many gaps that can be filled before going to interviews. In doing so, you will also sound fluent using the correct terms and feel confident explaining your work.
2- Learn from others
If you are serious about getting a job as a Data Scientist in your first couple of months, then you should learn from those who have a lot of experience. Teachers and teaching assistants are excellent sources of information, so speak to them daily. Ask a question about recruitment processes, interviews and how to manage conversations with recruiters to find out more about the company and the role.
Also, I’ve created a slack channel with two other bootcamp alumni. In this channel, we share our CVs, cover letters, feedback from interviews and tests. We’ve discussed interview questions and answers, and we are always sharing our codes and notebooks to help each other. Don’t be afraid to share your work, instead learn to work together. After all, you have the same goal: to become a Data Scientist — asap.
Photo by JESHOOTS.COM on Unsplash
3- Motivation to learn outweigh coding skills
It should come as a surprise to any recruiter that you don’t have ‘commercial experience’ as a Data Scientist. Just by looking at your CV anyone can tell that you are seeking your first job. That said, don’t try to sell yourself as an expert Data Scientist (from Kaggle projects), this is not your most valuable skill at this stage.
After I got an offer from Nextdoor, the HR manager gave me feedback from each of the eight interviews I had done. It can be summarised in one ‘pro’ and one ‘con’: I am eager to learn but I don’t have coding experience. What I have learned is that hiring managers are looking for people who are keen to learn new things and keep up with the industry.
So, show that you are a curious person, you enjoy the process of learning data-related topics, and you practice coding every day. Demonstrate your passion for the field of data, computer sciences, statistics. Your motivation and commitment to continuous learning will (and should) outweigh your current coding skills.
4- Know what you want
Knowing what you want without ever having experienced it is somewhat abstract. How do you know you want to be a Data Scientist but not a Machine Learning Engineer or a Data Engineer or a Data Analyst? At first, all those positions seem very similar and perhaps you would accept any of those as your first job. Well, that is what I thought in the beginning, and it is a mistake.
The key difference at the job-seeking stage lies in the preparation for interviews. If you know that you want a Data Scientist job, make sure you know exactly what Data Scientists do. As you research, some nuances will start to stand out. For example, Data Scientists tend not to use Tableau, used by data analysts, or Docker, used by Data Engineers. You don’t have to develop a wide breadth of Data Science knowledge, instead, you could improve the depth of what you will need in the new job. Some examples include Pandas, Numpy, Scikit-learn linear and logistic regression, matplotlib, and seaborn. If you master these, I am certain you will get a Data Scientist job very soon.
5- Get used to rejections
I cannot stress this enough: please, get used to being rejected by recruiters, hiring managers and companies. At the beginning of the process of seeking your first Data Scientist job, you are highly motivated and nothing can stop you.
However, as the weeks go by and the rejections keep popping in your inbox, your motivation level inevitably crashes. There are plenty of Data Scientists roles out there, as well as an increasing number of candidates. Also, hiring processes are slow but much slower from a candidate point of view. I received rejection emails after two months in the new job. Anyway, it is natural to get rejected.
An idea to keep your motivation high is to share it with a group of friends who are going through the same process. Like I have said before, create a slack channel with other alumni and share your frustrations. I am sure they are also going through the same thing. This is important because you will notice that you are not rubish at coding, it is just a matter of time, consistency and effort.
Never give up!
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Bio: Renato Boemer is an impact-driven data scientist professional, with an innovation and marketing background, which enables him to combine statistics, programming, and business insights.
Original. Reposted with permission.