Looking for your first job is a classic chicken-egg problem. You can’t get a job because you don’t have the experience, but to get an experience you need a job. While you look for a data scientist job – out of college or switching career, this will be a hurdle. This article outlines a few tips to aid you in getting your first data science job.
Working in data science requires an advanced skill set. At the beginning of studies, you are expected to master algorithms, become proficient in programming, acquire advanced knowledge of statistics, and much more. As you continue, you are expected to update your skill set. You are required to understand the problems of the organization and continually find ways to solve them. This will be a continuous work without a break to increases your chances of getting your first data scientist job.
Keep your technical skills on point
By far data science is the most sophisticated technical field to master. It requires knowledge of multi-disciplinary areas – statistics, machine learning, discrete mathematics, programming, and much more. Each area is extremely broad and requires ample practice to get the hang of it.
If you have a STEM background, it would be relatively easy to prove your competence in data science. However, if you have a non-technical background taking one of the industry-recognized best data science certifications online would be an ideal option. Some globally recognized certifications are offered by DASCA, Dell, and Microsoft. These certifications validate your skills, without having to unnecessarily push yourself to catch recruiters’ attention.
Build a portfolio
Data science is a high application field, meaning either you can do a task or not. There’s no grey area. Companies rely on data science professionals to get work done and portfolio is the best way to assess a candidate’s ability to get work done.
There are numerous platforms, where you can work on data science projects including building models – Kaggle, Data-Driven, Github and host them. You can work on ample projects on these platforms. Try to do projects from each area of data science cycle — collection. Cleaning, analysis, visualization, and modeling. This gives the recruiter a better view of your skills and comes up with relevant opportunities…