There are many factors that go into the making of a data scientist and the reflection of all that is your data scientist resume. On an average, a candidate spends less than 90 minutes to create a biodata. For an average data scientist resume, a candidate takes anywhere between 6 to 8 days, which is way above the standards observed in the hiring industry of the past 7-8 years. Yet, only 1 out of every 10 certified data scientists ever makes it to the interview selection round in the first attempt.
In our article on the data scientist fresher resume sample, we provided a comprehensive guide on how to expertly write a CV for your data science job.
In this article, we will go a notch deeper and explain the biggest mistakes that candidates make in their biodata and how these could be easily corrected for your benefit.
Not Creating Multiple Data Scientist Fresher Resume Samples
This is the biggest mistake that freshers make while applying for the job. They use the same biodata everywhere, applying without giving a thought to the actual demands of the hiring company.
You should ideally create two sets of CVs. Each CV should showcase your expert knowledge of a statistical computing language such as Python or R. The main CV format should categorically mention your projects involving work with probability and statistics, including experimental design, predictive modeling, optimization, and causal inference, experience in design / deployment of real-world commercial user-facing systems.
A shorter version of this high-detail CV should be available with a clear overview of your research on Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau, big data).
Not Focusing on Industry Keywords
Data science keyword research is very important for CV making.
Imagine you are interviewing for a top tier Big Data Company that services to another enterprise company like Google, Amazon, or Microsoft. They could be looking for Data Scientist specifically for Amazon cloud and data management. In that case, you should skim your CV for Amazon related technology and product keywords. Your projects during the internship could have some very specific keywords that could be relevant to the Hiring Manager at Amazon and therefore, earn you more points in the selection round from thousands of candidates applying for the same role.
Putting Mono-dimensional Data Science Qualities
Of course, a data science resume is not everything that guarantees a job for you. But, it’s everything that can get you the visibility in the hiring industry, especially if you are eyeing your first job, or switching from a non-data science role to a hardcore analyst role after many years of experience.
First time applicants make this common mistake of putting all of their data science skills in one basket, leaving no room for the hiring managers to decide if the candidate has any special soft skills and qualities that would impress the interview panel.
Add your skills that fetch beyond the usual conundrum of data science, mathematics, and logical reasoning. You could provide your work in the fields of Art, Commerce, Environmental Science, or Sports. I have seen CVs that contain detailed information on bug bounty hunting, Netflix binge watching (Yes, it’s a skill that data scientists and analysts used initially to create Netflixes of the current era!!!)
Why do you think we have so many live streaming and Video on Demand apps suddenly sprouting up in such a short span of time! They hired data scientists who did re-engineering and reverse engineering to think out of the box and come out with great ideas in Recommendation Engines and Real Time audience streaming experience management.
According to a leading data research company that provides information on the job market and industry trends, Hiring Managers and advisors to top data science companies prefer to pool in CVs from data science professionals who have mentioned their information accurately and are able to demonstrate their professional experience through their biodata adequately. It could be reflected in terms of word count or in years of experience, in a single line. It all boils down to the CV formatting and accuracy of the information provided.