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Get Hired By Passing The Data Scientist Interview Exams
Join Isaiah and Kasey Hemington as they discuss the Data Scientist hiring process for PhDs.
Here’s a quick rundown of this week’s episode…
- First, Isaiah interviews a panel of PhDs who successfully transitioned into Data Scientist careers.
- Next, the expert panel discusses what it takes to transition from a PhD to a Data Scientist.
- Finally, Isaiah and Casey explore the intricacies of the data scientist job hiring process, such as building an impactful portfolio and the seven keys to get hired.
From This Week’s Show…
Advice From Fellow DSS Associates
While dealing with big data, I suddenly found myself intrigued with its importance, which made me rethink my original objective of pursuing a PhD. This rendezvous inspired me to explore and teach myself the technical skills needed for the data science position.
Coming from a background in bioinformatics, I needed to find a position in industry where I could use my skillset. After a lot of soul searching and, following the CSA advice of trying to find my desired lifestyle. I zeroed in on the data scientists arena and then started gunning for it.
I faced initial challenges, especially because I felt I was separating from my background. But then I realized that all PhDs are data scientists at some point, combining the realms is just a way of making me better in what I do.
Filling In The Gaps To Transition From PhD To Data Scientist
I was looking for a force to motivate me beyond my industry transition. So, I joined DSS and perceived that industry has a high demand for my knowledge in data and science. Although I had some technical skills, I did not have transferability. exposure to the DSS course made me realize what was needed to make myself more relevant to the industry position I was trying to get into.
To overcome your initial challenges, you might have to learn new programming languages or know how to translate your ability to analyze information and data into logical output that let people make business decisions. You also need to highlight your transferable skills or business acumen skills. You have to relate to industry plans or business questions because they come from quantitative background.
Although we have worked with several different software programs, doing it professionally in industry is quite a bit different. For maturing into the industry role, one has to get rid of that academic mindset, the academic nomenclature and get trained on the industry mindset and nomenclature for the role.
It is crucial to decipher what are the gaps that we have in our knowledge and which real world experience can close those gaps. The DSS program is particularly helpful to determine what to do on a technical call for the role, How to face a take home exam, and how to answer behavioral questions.
When you look online, there are hundreds of courses you can do, a hundred ways of learning data science too. But there is a very unfocused way of doing things, especially when you are not sure what the end game is. So, a big gap in my knowledge was, what really is a data scientist. Because on many levels I’m already a data scientist, right? As a PhD I look at data all day, I analyze them all day. Am I not already doing it? So, that was one big gap in my understanding: what was the difference between doing data science in industry and in academia?
The Seven Keys To Getting Hired Into A Data Scientist Role
So, we have a list of the seven keys that we’re going to go through. Of course, it is important to portray your value, whether it’s in your resume, LinkedIn profile, portfolio, very specific to the data scientist role, take home exam.
Since the data scientist role is a comparatively newer role in, there is a lot of variability associated with it. This variability and at times ambiguity may throw people off during a job search. Therefore, it is pertinent to keep an open mind and ask a lot of questions to figure out what sort of situation you are in with different companies in different interviews.
So let us go through what the job search process looks like, which are the trending topics to really add to your understanding and your portfolio. So, let’s take a look at the LinkedIn profile. I think one thing people don’t think about enough for the data scientist role is, how important it is to highlight your projects on LinkedIn.
I think it is important to know that data scientists will still be required as automated data science becomes more popular because the business aspects of the problem statement, the conclusions that you generate are indispensable important aspects of the process.
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