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When Did It Become So Difficult To Land A Job In Data Science?

When Did It Become So Difficult To Land A Job In Data Science?

The analytics job scenario in India has improved substantially. Even with the skill enhancement being the chief priority among employers and leading enterprises, the hiring process still leaves IT professionals befuddled. According to a Michael Page Job Applicant Confidence Index report, Indians have the highest job confidence outlook and around 47 percent of working professionals expressed the desire to develop new skills.Despite the high confidence and positive job outlook, the 21st century’s sexiest job is not easy to land. The field is fraught with the hype that data science is something one can achieve, simply with a string of online courses or specialisations, without any prior industry experience. The result is that there are analysts who have overstated their SQL skills on their rèsumès or even oversold their skills to snag an entry-level position but have quickly gotten fazed by the realistic demands.

There are different (informal) rules for different candidates. Mid-level or senior professionals with five or more years of work experience under their belt usually do not have difficulty in clinching a Data Analyst or Analytics Consultant position as much as a fresher. For inexperienced candidates, it is an uphill task, especially those who have a business degree.  

Of late, with data science sector gaining steam, there has been an influx of graduates and early-stage professionals from business, marketing and IT background who want to pivot to this field. But despite retooling, they realise that they are not getting what the hype led them to believe. The field of data science requires years of experience and expertise in multiple areas.

The trend of short-term courses has spawned thousands of students or graduates landing well-paying position in the analytics industry. However, freshly graduated students who lack industry experience find it highly demoralising when they are only considered for internship positions. Even their fabulous tech-intensive projects are not enough to get them an entry-level position.

Here Are A Few Hindrances That Freshers Face:

Industry Experience: It is true that talent shortage affects both types of companies — startups as well as enterprises. However, a common hiring trait that both types of companies share is their emphasis on prior industry experience. A look at job postings on Indeed or other popular job portals reveals a requirement for at least 2-3 years of experience in a related field. Companies should be able to correlate the candidate’s performance on coding tests and technical interviews and understand their raw ability and potential.

Bar Is Rigidly High: Are companies looking for Facebook or Microsoft-level talent when it comes to pushing the envelope. However, graduates who bridged the learning gap with MOOCs or full-time undergraduate programmes usually approach the subject from a tools-first perspective. This is unlike the university-backed courses or real industry experience, where one applies techniques and methodologies in a way that can address problems in a clear way which can eventually inform and influence the decision-making process. In short, the right candidate is expected to have relevant statistical nuances to understand the data.

Recruiters Are Getting Smarter: There is a saying that data analysts are born, not made in tutorials. Just knowing tools and programming languages or structure is not enough. In fact, recruiters look for a demonstrated history of the application of tools and techniques. Also, there is a lot more competition at the entry and intermediate levels since professionals from different career paths want to break into this field. That’s why, a strong data science portfolio (mix of ML project, data visualisation, exploratory data analysis projects) helps in demonstrating the skills and qualities needed for that particular role.

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Going Beyond Titles Like ‘Data Analyst’: There are multiple positions opening across the industry and one must also look for titles or positions like Research Analyst or Business Analyst.


Our annual Analytics and Data Science Jobs Study 2018 indicated how rather than basing their recruitment on technology or tools, recruiters are basing it on skills. In other words, there has been a significant decrease in the jobs advertised for a specific tool. Analytics recruiters are coming to the conclusion that unlike IT, analytics requires a combination of various skills, and tools are just one aspect of it. Among all analytics recruiters, the demand for Python professionals is the highest.

Almost 39% of all advertised analytics jobs in India demand for Python as a core skill. Python also saw the biggest leap in analytics requirements this year, replacing R as the most sought-after analytics tool. And 25 percent of all analytics jobs are looking for professionals skilled in R, making it the second most popular data analytics skill. This is a decrease from last year’s 36%. Meanwhile, Tableau skills are most in-demand among visualisation tools.

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