Why Data Science Might Not Be The Right Career For You?

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Data science might be ‘the sexiest job of the 21st century’ with fat salaries, but that does not mean it is the right career choice for you.  As per AIM Research, 1,400 data science professionals working in India are paid more than INR 1 crore. According to AI & Data Science Salary Study 2021 by AIM Research, the salary of a data analytics professional is 44 percent higher than that of a software engineer, and 36 percent and 50 percent higher than the salaries of an IT developer and a Java developer respectively. 

Data science is about defining and solving business problems. However, many experts claim there is nothing wrong with people choosing this career for a better life, and they can always learn on the job and grow. 

Unfortunately, many candidates applying for data science roles are drawn by the glamour quotient and/or monetary benefits, and in most cases don’t have the right skillset to excel in the field. 

“Everyone cannot be a data scientist or an analyst. So, we have to look at the right fit and where it starts. We can not start from the point where the candidates join the organisation. It should start within the campus or colleges,” said Deepak Kumar Arora, head of learning and development at Birlasoft, at a roundtable discussion on ‘analytics as a driver of talent transformation,’ organised by AIM and Jigsaw Academy.  

Mathematics and coding (R, Python, C++, Java and others) are essential skills for a rewarding data science career. However, it is not necessary to know all the programming languages.

Candidates should also have excellent communication skills and gel with their teams. They should know social media mining, SQL/NoSQL, natural language processing or machine learning algorithms, Microsoft Excel; and most importantly, need to be data and business savvy. 

PhonePe’s data science head Kedar Swadi told AIM that candidates often focus on the currently fashionable techniques (like assuming they only need to understand deep learning) rather than having a solid foundation in algorithms, maths, and statistics. “A lot of candidates also list techniques they have only used, rather than listing those that they have a comprehensive understanding of (in terms of how they work, what assumptions they make about data, how they should be evaluated, what impact turning parameters has, etc.),” he added. 

He said candidates fail to understand or appreciate the business aspects of problems. Also, candidates lack exposure to the engineering aspects of developing solutions. 

Most people go into data science for the adventure it offers. However, the reality is slightly different. “In most organisations, you’ll have to spread your time between doing technical work and the other, less exciting stuff, ” said Adam Sroka, head of machine learning engineering at Origami. 

So, if you are not keen on reporting, writing, documenting and delivering presentations, or repeatedly explaining the basics of your models or techniques, project management, administrative overhead, etc to the stakeholders, then the job might not be a right fit for you.

Candidates coming from an education or research background often fall into the trap of infinite timescale and infinite budget mindset. “All too often, I have heard protests from data scientists saying they can not put a ‘timeline’ on when their work will be finished, and it will take as long as it takes. This simply is not true and won’t fit well with the culture at most organisations,” said Sroka. 

‘You either fix the scope of what you are trying to achieve and vary the timescale, or fix the timescale and vary the scope,” said Sroka. 

Communication is pivotal to forge a successful career in data science. For instance, if you are working closely with the company’s decision-makers, maintaining a solid relationship is essential. 

Most importantly, you will have to maintain a good relationship with all the team members across departments, not just senior management. Always look for an opportunity to solve the business problem or in-house team concerns to the best of your ability: be it automating redundant tasks or basic data retrieval.

Most data science professionals in a company, by default, will be considered analytics and data experts. 

However, Jonny Brooks-Bartlett, a data scientist at Deliveroo, said trying to tell everyone what you know and have control of can be difficult. “Not because anyone will think any less of you, but because as a junior data scientist with little industry experience, you will worry that people will think less of you,” he added. 

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