Posted by Tapan Bhatt on December 03, 2017
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“Nearly all big tech companies have an artificial intelligence project, and they are willing to pay experts millions of dollars to help get it done.” – The NY Times

The NY Times recently wrote about the lack of AI (Artificial Intelligence) talent (data scientists):

“Tech’s biggest companies are placing their future on artificial intelligence… As they chase this future, they are doling out salaries that are startling even in an industry that has never been shy about lavishing a fortune on its top talent… Solving tough A.I. problems is not like building the flavor-of-the-month smartphone app. In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research”  - New York Times 22 October 2017.

The NY times gets it right as they highlighted a challenge that most companies face. I think this challenge is bigger especially with companies that are small to mid-sized. Data Scientists are a small cohort of people, and the best of them are recruited by top tech giants, leaving very little talent free for companies that cannot afford to pay dearly for the best.

I have also come across companies (some of them now Teracrunch customers) that attracted this talent, but only temporarily as they were not able to keep them. Data Scientists easily get bored and are constantly poached with lucrative job offers. By the time they start to produce results, chances are they may leave.

Besides hiring AI talent, the cost of having to choose from and integrate the multiple technologies needed for AI work is often underestimated. There is no off-the-shelf solution available to solve niche customer problems; there are an overwhelming number of platforms, tools, and models to choose for the development. Many of which are open source and therefore not fully supported, and are error prone.

Given the scarcity of talent, development complexity involved and lack of standardization, this field needs proven data science methodologies. Methodologies would be meticulously crafted steps and techniques developed and vetted by a team of data scientist. A methodology with associated algorithms, functions and an experienced team of data scientists is the recipe to drive success and ROI. If done right, research says data science brings $13 for every $1 spent. Without that it will be a very costly failed experiment.

Tags: Technology

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