Data scientists need to swap sex appeal for power

Data scientists need to swap sex appeal for power

Data scientists need to swap sex appeal for power

If you go by the latest headlines, the data scientist is the most coveted and scarce enterprise commodity on the employment market. But you could just as easily argue that it’s a trendy title that has not nearly lived up to the hype — and could eventually be automated out of a job.

According to a recent McKinsey Global Survey, 86 percent of executives said their organizations have been “at best only somewhat effective in meeting the primary objective of their data and analytics programs,” and one-quarter said they’ve been “ineffective.” Less than four years after being named the “sexiest job of the 21st century,” it’s time to confront some cold, hard realities. Data scientists are both extremely important and set up to fail unless they adapt to a new model for delivering value.

Recently, I was invited by the UC Berkeley School of Information to host a conversation with students and alums on the real-world applications of data science. During the Q&A, my favorite question was whether I thought software like ours at Alpine Data might eventually replace the data scientist all together. My answer at the time was “no,” more or less: Data science will always be something of an art.

Read Also:
Big data takes aim at pediatric cancer

You need to intimately understand the problems that can be solved by data science first, which involves a very human process of interacting with the business. Crafting models will always require the subtle translation of real-world phenomena into mathematical expressions. And there is a human element to interpreting and presenting results that would be difficult to automate.

But it’s still true that, over time, more aspects of a data scientist’s work will be done by software. Feature generation has already become less important as models become more sophisticated. Model parameter selection will become increasingly automated — model deployment entirely so. It seems inevitable that the job description is going to evolve.

Consider how the work of the software engineer has changed fundamentally in the last 20 years. They no longer need to write their own logging module or database access layer or UI widget. And agile methods have brought the “customer” more immediately into the development process. More and more, the job of the engineer is to stitch together higher-level components and collaborate with product managers and UX designers.

Read Also:
Federal policy for self-driving cars pushes data sharing

Similarly, the job of the data scientist will be to take advantage of pre-built components in order to solve a greater variety of business problems.

 



Chief Analytics Officer Europe

25
Apr
2017
Chief Analytics Officer Europe

15% off with code 7WDCAO17

Read Also:
Machine Learning For Drug Discovery

Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
Machine Learning For Drug Discovery

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
3 Steps to Profit With Shared Data Experiences

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
How To Hire Analytical Workers: 10 Traits To Seek

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
3 Steps to Profit With Shared Data Experiences

Leave a Reply

Your email address will not be published. Required fields are marked *