Jobs in analytics: Why it pays to be data savvy

A state education ministry approached data science consulting firm Gramener with a request: To analyse marks of school children based on their demographics, so it could be known which student requires special attention or is likely to perform well and required action can be taken.

Instead, the analysis brought forth something fishy. “A large number of students got exactly 35 marks – the passing threshold - in English in Class X,” Anand S, CEO and co-founder tells me.

They went back to the ministry with an unanticipated conclusion: Clearly at the pass mark, there were a large number of students and these had been scooped up from 34, and 33, and 32, up to 35, effectively being given grace marks.

But why was it that there were still some students who failed at 34? Did the English teachers just decide that they don't like a particular student? "What this told us was that there was potentially room for an unfair policy, and therefore was actionable and it. was happening at the scale of an entire state."

Against a chart which allows people to interpret things for themselves, Anand says analytics is about pointing them to the salient features of the result and providing a takeaway, which is what constitutes a story path.

Dubbed the "sexiest job of the 21st century", the role of a data scientist encompasses collecting large amounts of data, cleaning and analysing them for patterns and finally interpreting and communicating the findings via visualisations. Notwithstanding the impact of COVID-19 on the job market, there were more than 93,500 data science jobs vacant in India at the end of August 2020, a report by edtech firm Great Learning found.

What’s pushing up the demand for this role? There is a growing awareness that data can do wonderful things. But there’s also a catch, says Anand.

Read on to find out:

Q] Which sectors have been seeing rising demand for data scientists and analysts?

A] The most surprising increase has been in the environmental sciences. Retail has always had a lot of data and now they're taking it up a notch. Banking, telecom, similarly. Manufacturing has consistently had slightly less data, but they're leapfrogging with IoT and telemetry.

Q] An earlier study mentioned the role for data scientists and analysts in India has jumped 45% in just 12 months over 2019. But there was not enough talent available.

A] Imagine I have a job posting for a data scientist and I have a job posting for an analyst. What do you think people will apply for?

DJ: Analyst.

AS: Why?

DJ: Because, when you say data scientist it signifies some specialised expertise.

AS: Therefore I may not be able to get there?

DJ: Probably.?

AS: Now, if you read this after you saw the HBR article that said data scientist is the hottest job of the next century, what would you apply for?

DJ: I would apply for the data scientist post and then work on upskilling myself.

AS: That’s what I see a lot of people doing. They say, data science is the job of the 21st Century, so I don't know what it is, I don't care what it is, but that's what I'm going to apply for.?

So what's happening is people (employers) are systematically renaming their analysts as data scientists. People are doing it for themselves and for their jobs.

Similar thing happened with programming. When programming became the craze, a number of people were using spreadsheets, 123, Excel, etc. But they were applying for programming jobs as well, and were getting through too. So a lot of relabelling is happening and that's contributing to a chunk of the growth.

The other part of it is real. There is a growing awareness that data can do interesting things, and therefore the number of jobs is increasing. People are doing whatever it takes to be able to be a data scientist and therefore the demand is growing. But there is a mismatch. Just by naming myself a data scientist I don't become one.

Q] What’s the pay in this field like?

A] Higher than an analyst. A data scientist is expected to have more skills including programming, storytelling, business knowledge. Reality is most people don't have those skills and therefore what's happened is the term data scientist has become raised both from a demand side and a supply side pulling up the salaries of people who have analytics capability.?

Q] What is the one big challenge that companies typically face during hiring?

A] Signal versus noise.

I had a rule of thumb, about 6-7 years ago when hiring programmers. If they had a GitHub repo, I told the HR team to send the resume straight to me. Because nobody would put their repo on to GitHub unless they were proactive, really enjoyed programming.?

Today, every college uses GitHub to teach people Python, to do data science, so now this has become the norm and therefore I need to look for things that are different from a signal perspective.

Q] What advice would you give to job applicants?

Prove that you've gone beyond what you've been expected to or asked to do. If you're working in a company, show me where you've done something that you didn't need to. If you're a student, show me what you've done that's outside your curriculum that demonstrates an interest.

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How has demand for data analytics increased over the last year? Share your views in the comments.