Continuous advances in AI will enable data scientists to innovate and execute evermore complex tasks

In 2012, data science was described as the ‘Sexiest job of the 21st century’ by Harvard Business Review. Six years later, is it still true? With explosive growth in the volume of data in recent years and the resulting disruption in business, it’s no surprise that the ability to capture, analyse and use data — particularly to power artificial intelligence — continues to be highly sought after.

The data scientist role has become an increasingly critical one, uncovering patterns and insights that help businesses stay competitive by allowing them to quickly respond to trends and customer needs. While still sexy, the role has most certainly evolved. There no doubt remains huge demand for data science talent and employment opportunities abound, so it’s worth looking at what is required for data scientists, now and in the future.

What made data science so sexy in the first place, however? Initially, it was the realisation — first by technology companies — that access to and analysis of data was going to have a huge effect on business. Then, it was the limited supply of talent, making data scientists highly prized assets. Finally, data scientists were given a large amount of autonomy to dive in deep, taking the lead on research projects that might change the way their organisations conducted business. Like other ‘sexy’ jobs, in data science, there is a limited number of people who can do it well, scientists must venture into the unknown like explorers and astronauts, and they can command significant compensation.

Also read: How to build an in-house data science team (without a data scientist)

The Evolution of the Data Scientist

Data science has always meant using data to generate insights and identify solutions for real-world business problems. For example, Appier’s AIQUA product is an AI-powered platform that analyses changing sales patterns and pushes recommendations to buyers based on their demographics or usage patterns. With more precise data analysis, companies can apply data more effectively — removing guess work — and see increased conversion and sales.

So how has data science changed since it officially become sexy in 2012? Most notably, there is a much great volume of data available, even in the past six years. About 2.5 quintillion bytes of data are now generated every day, giving data scientists a vast pool within which to dig up treasure. Additionally, just a few years ago, it was primarily technology companies who were employing data scientists, but now data is powering decision-making across all types of industries and businesses.

The ability to make sense of data to identify financial or consumer insights means that data science talent is still in high demand. A September 2018 study by LinkedIn showed that data-scientist jobs in Singapore recorded the steepest rate of growth (17 times) between 2013 and 2017. This demand means that data scientists are typically well compensated as businesses are keen to attract and retain them given there are so few, and the scarcity of talent means the role retains an air of exclusivity.

The key for organisations looking to hire the best data science talent is to make sure they have a suitable AI infrastructure in place. One of the biggest challenges that data scientists face is that their employers become frustrated when they don’t see immediate returns. To identify critical insights, a good data scientist must carefully look at a huge amount data. The right technology must be in place to allow them to do this properly, or the best data talent will leave for companies that are investing in the most effective AI tools and platforms.

At Appier, our data scientists work together on enormous amounts of data and identify insights to help our customers close the gap between users and services. They understand that both technology and consumer expectations are evolving rapidly, and that they must always be open to learning new things and using data in creative ways. Continuous advances in AI will enable data scientists to innovate and execute evermore complex tasks.

Also read: Looking under the hood: How Grab’s data science team optimises a fleet of 2.4 million drivers

The Current and Future Pillars for a Data Scientist

Data scientists come into play when a company’s data volume surpasses a certain scale and requires a model to create products to analyse it.

A good data scientist should be:

Adaptable: Data scientists must be willing to constantly upskill themselves to master advanced machine learning skills such as deep learning. While technical skills are fundamental for data scientists, it’s crucial for them to master communication skills too so they can easily interact with domain experts or business developers. Data scientists will need to develop a better understanding of the overarching business strategy and business challenges in real-world scenarios to create solutions for real problems.
Statistics at the heart: Data scientists must have quantitative capabilities to figure out multifaceted trends within a data set that may entail more than one million rows.
Detail-oriented: Data often have errors and discrepancies, and data scientists must identify and correct incomplete, incorrect or inaccurate data. It’s critical that data are clean, high-quality and unbiased to ensure the best output upon which to make business decisions.
Good programming skills: Programming skills, together with statistics, are critical. For statistical analysis to happen, data scientists need to know programming languages (such as Java, SQL, and Python) to break down the data set in more digestible formats.
Business knowledge: While it is important for data scientists to be technically capable, they must also be business savvy and understand the organisation’s business goals and objectives, so they can analyse the data to support business success.

As AI advances and more low-level tasks are automated, the expectations placed on data scientists will continue to grow. There are huge opportunities to enter the field, and many organisations will be willing to pay the right price and above for the best talent with the full package of skills and smarts. Data scientists will be able to choose roles based on how interesting they find the particular data challenges, and they’re going to be needed in every industry- from technology to insurance, healthcare to travel.

The role of data scientists is and will remain a sexy profession for some time, partly due to its relative exclusivity, and the field of data science itself will no doubt remain an exciting space. Just like great pioneers before them, the most important thing a data scientist can bring is endless curiosity and a passion for learning as they continue to explore uncharted territory.

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This article was first published on e27, on October 29, 2018.

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