Big Data and Data Science. Some reflections on compensation levels

Big Data and Data Science. Some reflections on compensation levels

Big Data and Data Science. Some reflections on compensation levels
I was at a meetup in Oxford recently and one of the speakers, the CEO of a tech start-up, brought up the subject of Data Scientists’ pay. Apparently they are paid too much. I am not sure whether the data supports this assertion, but it seems to be a common complaint amongst highly-paid CEOs. What surprised me was his tone of moral indignation: How dare an engineer demand to be paid as much as a general manager? He was really upset.

I guess it is ingrained in British culture that if you want to earn more, you stop doing what you are good at and become a general manager. But it got me thinking. What determines how much someone should be paid? What has changed so that Data Scientists can ask for more? And can it last?

After 2008 no one still believes that value creation determines pay. Clearly it must determine the sustainable upper bound of pay. But given that you can only be paid less that you contribute, how much less? It all comes down to bargaining power.

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Currently there is a lot of demand for people who can build data products. Companies like Google and Uber have shown not only that you can make money by embracing data science, but also that you will become vulnerable to disruptors if you don’t. Small companies want to emulate Uber. Big companies want to fight Uber. To do so they need data scientists. But the supply of data scientists is inelastic. It’s a new technology, and only a subset of existing data professionals (analysts, architects and developers) have the skills or aptitude to adapt.

But demand exceeding supply in itself doesn’t drive individual wages up.

It’s not unusual for a firm to have one employee who is said to be “irreplaceable”. This is the one guy that has been there from the beginning, that understands the product and the technology, that the company couldn’t function without. On the face of it that employee has the same level of bargaining power as these star data scientists. Unfortunately the same knowledge that makes him valuable also acts as his prison. His knowledge is only useful within that company. In any other company he has no special value. He has no outside option and so no credible threat of exit. So he has no bargaining power at all and is often lowly paid.

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Why would data scientists be any different? They too are technical specialists, but unlike engineers in industry, the data scientist’s skills are much more easily transferred between businesses. I think this is because they work at a level of abstraction which is not tied to a particular product but more to a set of concepts and methodologies.

But it is this same kind of abstraction that has capped the wages of software engineers, who are at threat of being outsourced to India (or Romania or wherever) if wages escalate. They can be outsourced precisely because the same set of concepts and methodologies can be abstracted and applied anywhere on the planet.

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