This was the subject of a provocative article posted on Oracle's blog, two days ago. It certainly shows how far from the reality some big companies are. They confuse people who call themselves data scientists (or get assigned that job title), with those who are true data scientists, and might use a different job title. Many times, the issue is internal politics that create the confusion, and not recognizing a real data scientist with success stories to share, or not leveraging them.
Here is some food for thought from Vincent Granville and @JakePorway. You can add yours in the comment section below.
- Someone who is unable to provide added value on data is NOT a data scientist, as one of the core components of data science is creating added value on all sorts of data. The bubble in question is the fake data scientist bubble. Real data scientists (like me) generate significant value. They are a dime a dozen, but rarely found in large companies. In my case, I created and manage my own company, and much of the added value is created thanks to automated data science.
- Also, sometimes, the bureaucracy and politics prevents a true data scientist from delivering full value, if she is in a nasty environment, her team is not respected, she's not listened to, has no power, or she is dealing with executives who are totally clueless. Too many times though, we still see PhD's in their ivory tower, unable to deliver value, not understanding the business model, miscalculating the risks, no cooperating with other departments, and focused on beautiful models, rather than stuff that works. These people are not data scientists. They might produce great websites such as Yelp, not knowing that it is infested with fake reviews to the point of being useless. And unable to notice it, or to fix it.
- Any data scientist worth their salary will say you should start with a question, NOT the data.
- A data scientist should navigate across silos, not be confined to one silo, unless working on a very specific, narrowly-defined project. Even though, he/she should seek external data sources, as needed. Data scientists strive better in small to medium-size companies or departments, in flexible companies, as consultants, or as entrepreneurs.
- There are two types of companies: those that see analytics as an expenditure, and those that see it as an unfair competitive advantage.
- True data scientists are also sales people: (1) they promote techniques that are sellable (during their job interview or during corporate meetings), then (2) they sell it at a fair price (measured in numbers of hours and resources to complete the project) and (3) they finally deliver! In my case, I promise a lot of great leads to potential clients. I deliver beyond expectations, and I use back-end data science techniques to generate high volume of high quality / relevant traffic (the client does not even need to know that data science is involved). Then I help the client measure the yield, to make sure that traffic generation is correctly attributed to our (automated) efforts. I get paid after delivery, and have tons of return clients, small and big.