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Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job – What is the difference?

Do the usual (attending data camps if you don’t have any experience), and you will go nowhere due to competition doing the exact same thing as you. Do the unusual, you will go nowhere either in terms of landing a job, as nobody understands what you do. However, the big difference is that in the latter case, you can compete with employers who won’t hire you, and you can eat their lunch. That is what Uber, AirBnB, PayPal, Google, Zillow (predicting home values) and many more did, all of them massively exploiting data. You can do it with or without VC funding.

In my case, I am revolutionizing the world of publishing, making old business models (and many new ones) obsolete. None of these companies who would benefit from hiring me is contacting me (except to write a book with them, I have no idea what gives them the impression that I would ever accept)  -  their CEO’s, board members and investors don’t even understand my business model, and I love it (that’s how you start building a monopoly, when no one knows exactly what you are up to, and you take advantage of it to grow fast before anyone figures out the secret and it is too late for them to catch up).


That’s how you can make big money and bring big value; any class you attend or book you read to succeed, might actually put you on the slow track, the one of very small returns ,  by teaching you things that impede your creativity. So what should I do to succeed, you might ask? You need to be creative, know how to identify and solve unsolved problems without burning tons of money, how to deliver and adjust to the market in a perfect synchronous way as it evolves (what successful stock traders do), how to find the right VC, employees (or automate) and partners, and know when to exit when you have to.

You may as well build the largest data-driven empire even without any technical degree. There are still so many low hanging fruits that no one can see (or is interested in) today, that if you see some of them, and can design a product around it, deliver and build and sell your new mouse trap, you could be the next billionaire. Examples abound in data science and AI:

  • detecting voter fraud (my understanding is that in many places they look only at your driving licence, and many people not allowed to vote have a legit driving license not different from those allowed to vote),
  • designing scores that are much better predictors of success for students applying for college,
  • creating a new currency not used for criminal activities unlike Bitcoin,
  • replacing the Alexa robot by one that can have meaningful conversations with people,
  • making data about medical procedures in US hospitals public by having patients posting their bills and experience (to help optimize healthcare costs)
  • creating easy-to-use dashboards (GUI) that even robots could use; currently these interfaces (think about using Facebook’s advertising platform), designed by geeks, are hard to use and change all the time.
  • creating an automated system to purchase ads on Twitter, Facebook, LinkedIn, and thousands of websites that are relevant to the buyer
  • detecting fake members on social networks, or members creating multiple accounts, or sharing paid accounts with other people 
  • automated financial advice, medical advice (diagnostics), tech support, program debugging
  • predicting where the best location is for buying a new home, based on your criteria (good school, weather, jobs, and so on)
  • creating a cab company (like Uber) but with driverless cars. or robot bees (drones) to replace dying bees (interestingly. the origin of the word drone is to describe a male bee – the real insect.)

Many applications already exist to solve these problems, but they are working poorly and could be significantly improved. Data scientists who are generalists and also have deep vertical knowledge in more than one area, might be able to launch these initiatives. It might be easier (faster) if you can get seed money, but  if you can do it with no external funding, that might be the best solution. Do you need a Ph.D. in data science to do this? It helps to get VC funding and trust from potential clients. I don’t know anyone who managed to launch this kind of business without a great degree from a good school (even myself, I have all the classical credentials — though I had to unlearn much of my training to succeed) but I think it is not impossible. Some of the most successful people (think about the new CEO of Microsoft) were not born in wealth. Think about it.

Also, you must be passionate about what you do to succeed to the point that you don’t even feel you are working, be perseverant, and have certitude that you are right when everyone tells you that you are wrong (by itself,a good sign that you are up to something, unless you are dreaming.)

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