Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Hiring vs. training data scientists: The case for each approach

Nov 22, 2017 | 570 Views

Hiring data scientists is easier said than done -- so should you try to train current employees in data science skills? That depends on your company's needs, writes one analytics expert.

Companies are faced with a dilemma on big data analytics initiatives: whether to hire data scientists from outside...

or train current employees to meet new demands. In many cases, realizing big data's enormous untapped potential brings the accompanying need to increase data science skills -- but building up your capacity can be tricky, especially in a crowded market of businesses looking for analytics talent.

Even with a shortage of available data scientists, screening and interviewing for quality hires is time- and resource-intensive. Alternatively, training data scientists from within may be futile if internal candidates don't have the fundamental aptitude.

At The Data Incubator, we've helped hundreds of companies train employees on data science and hire new talent -- and, often, we've aided organizations in handling the tradeoffs between the two approaches. Based on the experiences we've had with our corporate clients, you should consider the following factors when deciding which way to go.

The main benefit of hiring rather than training data scientists comes from introducing new ideas and capabilities into your organization. What you add may be technical in nature: For example, are you looking to adopt advanced machine learning techniques, such as neural networks, or to develop real-time customer insights by using Spark Streaming? It may be cultural, too: Do you want an agile data science team that can iterate rapidly -- even at the expense of "breaking things," in Facebook's famous parlance? Or one that can think about data creatively and find novel approaches to using both internal and external information?

At other times, it's about having a fresh set of eyes looking at the same problems. Many quant hedge funds intentionally hire newly minted STEM Ph.D. holders -- people with degrees in science, technology, engineering or math -- instead of industry veterans precisely to get a fresh take on financial markets. And it isn't just Wall Street; in other highly competitive industries, too, new ideas are the most important currency, and companies fight for them to remain competitive.

How a company sources new talent can also require some innovation, given the scarcity of skilled data scientists. Kaggle and other competition platforms can be great places to find burgeoning data science talent. The public competitions on Kaggle are famous for bringing unconventional stars and unknown whiz kids into the spotlight and demonstrating that the best analytics performance may come from out of left field.

Similarly, we've found that economists and other social scientists often possess the same strong quantitative skill sets as their traditional STEM peers, but are overlooked by HR departments and hiring managers alike.

Training adds to existing expertise
In other cases, employers may value industry experience first and foremost. Domain expertise is complex, intricate and difficult to acquire in some industries. Such industries often already have another science at their core. Rocketry, mining, chemicals, oil and gas -- these are all businesses in which knowledge of the underlying science is more important than data science know-how.

Highly regulated industries are another case in point. Companies facing complex regulatory burdens must often meet very specific, and frequently longstanding, requirements. Banks must comply with financial risk testing and with statutes that were often written decades ago. Similarly, the drug approval process in healthcare is governed by a complex set of immutable rules. While there is certainly room for innovation via data science and big data in these fields, it is constrained by regulations.

Companies in this position often find training data scientists internally to be a better option for developing big data analytics capabilities than hiring new talent. For example, at The Data Incubator, we work with a large consumer finance institution that was looking for data science capabilities to help enhance its credit modeling. But its ideal candidate profile for that job was very different from the ones sought by organizations looking for new ideas on business operations or products and services.

The relevant credit data comes in slowly: Borrowers who are initially reliable could become insolvent months or years after the initial credit decision, which makes it difficult to predict defaults without a strong credit model. And wrong decisions are very expensive: Loan defaults result in direct hits to the company's profitability. In this case, we worked with the company to train existing statisticians and underwriters on complementary data science skills around big data.

Of course, companies must be targeted in selecting training candidates. They often start by identifying employees who possess strong foundational skills for data science -- things like programming and statistics experience. Suitable candidates go by many titles, including statisticians, actuaries and quantitative analysts, more popularly known as quants.

Find the right balance for your needs
For many companies, weighing the options for hiring or training data scientists comes down to understanding their specific business needs, which can vary even in different parts of an organization. It's worth noting that the same financial institution that trained its staffers to do analytics for credit modeling also hired data scientists for its digital marketing team.

Without the complex regulatory requirements imposed on the underwriting side, the digital marketing team felt it could more freely innovate -- and hence decided to bring in new blood with new ideas. These new hires are now building analytical models that leverage hundreds of data signals and use advanced AI and machine learning techniques to more precisely target marketing campaigns at customers and better understand the purchase journeys people take.

Ultimately, the decision of whether to hire or train data scientists must make sense for an organization. Companies must balance the desire to innovate with the need to incorporate existing expertise and satisfy regulatory requirements. Getting that balance right is a key step in a successful data science talent strategy.

Source: Search Business Analytics
Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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