This beginner-friendly guide outlines how I prepared for data science job search, so you leave with a detailed action plan. From late 2017 to early 2018, I prepared and interviewed for my current data science role. Now, I get paid to train and deploy machine learning models (yay!) and use my combined statistics/econometrics and programming skills to create impact.
From late 2017 to early 2018, I prepared and interviewed for my current data science role. Now, I get paid to train and deploy machine learning models (yay!) and use my combined statistics/econometrics and programming skills to create impact. In this 2-part article, I share my approach that I think contributed the most to entering the field straight out of school, updated with real observations on the job.
Now, whether you are a fresh grad, seasoned data scientist, or looking to transition from another career, I hope this article can shed some light into what goes into a data science job search. Despite having been on the other side of the table now, as an interviewer, I still learn a lot from fellow data scientists’ experiences. My goal is for this article to be like an online coffee chat with you; there were so many people that shared their knowledge to me during my job search, and I’d like to pass it on!
This post was previously only available to newsletter subscribers, but I’ve taken the opportunity to update it with plenty of extra content, which ended up having 2 parts! I also encourage you to check out Serena’s excellent article about her data scientist job search approach, which inspired me to write one of my own. In addition, for those from academia (e.g. PhDs), check out Amir’s comprehensive LinkedIn article.
My opinion is that data science requires expertise in both statistics and programming. I do feel eventually more pillars enter the mix such as product, or systems design, but since this article focuses on my experience entering the field, I focus on these two pillars.
For simplicity, I use the term “statistics” to refer to machine learning theory, in particular, the mathematical understanding of how algorithms work, from logistic regression to natural language processing (NLP) techniques. Similarly, I will use the term “programming” to refer to general coding, building data pipelines, and software development (including version control, terminal, etc. if the role requires).
It is actually a tall ask to build expertise in both statistics and programming — oftentimes, schooling can only prepare one for so much. It could be that someone with a statistics background does not have the programming experience, and vice versa. For entry levels, my opinion is that one does not need to be a master at both statistics and programming at the time of the interview. However, you absolutely should not be a “zero” at either one of them.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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