Becoming a Billionaire Data Scientist vs Struggling to Get a 0k 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 and many more did. 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 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 mice 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), and many more.
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 tell you that you are wrong (by itself,a good sign that you are up to something.)
To not miss this type of content in the future, subscribe to our newsletter. For related articles from the same author, click here or visit www.VincentGranville.com. Follow me on on LinkedIn, or visit my old web page here.
Book and Resources for DSC Members
Comprehensive Repository of Data Science and ML Resources
Advanced Machine Learning with Basic Excel
Difference between ML, Data Science, AI, Deep Learning, and Statistics
Selected Business Analytics, Data Science and ML articles
Hire a Data Scientist | Search DSC | Find a Job
Post a Blog | Forum Questions
Link: Becoming a Billionaire Data Scientist vs Struggling to Get a 0k Job – What is the difference?