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Full Stack Data Scientist: The Elusive Unicorn and Data Hacker

This question was recently posted on Quora: What is a full stack data scientist? Below is my answer.

Most answers focus on the technical skills a full stack data scientist should have. This is only the tip of the iceberg. Put it differently, a full stack data scientist is also able to start a company, any company, from the ground-up, and leverage analytics in all aspects that makes a business succeed. It requires the ability to run a business, hire people, automate, find the tools, and outsource as needed to optimize business performance and client satisfaction. A full stack data scientist might not even spend time coding or have a degree in data science. Or he might write programs that write code to automate tasks. 


Among the qualities needed to qualify:

  • Strong business acumen and domain expertise in several fields.
  • Ability to leverage competitive, internal, external, marketing, sales, advertising, web , accounting, financial and various other sources of data, find the data and get it integrated, design the success metrics and get them tracked, and make decisions based on sound predictive models (with a zest of vision and intuition) to successfully operate any company, working with engineers, sales, finance and any other team.
  • Negotiation skills. Ability to raise money, internal or external, by providing convincing analytic arguments.
  • A PhD may help (or hurt in some cases!) but is not required.

A full stack data scientist (I prefer the word executive data scientist) is unlikely to get hired by any company. This is not the role hiring managers are looking for. Instead hiring managers call them unicorns and believe they don’t exist. In reality, they come from various fields, are not interested in working for a boss who will only allow them to produce code (but instead love to work with equal partners) and will compete with your company instead of becoming an employee — a role that is not suited for them anyway.

I hope this dispels the myth of the unicorn data scientist. Yes they exist in large numbers, but you won’t find them on LinkedIn if you are hunting for talent, and most — the successful ones at least — make more money and have a more exciting career than corporations can offer. In short, you are burying your head in the sand if you think they don’t exist, in the meanwhile they are eating your lunch, they are self-learners, move fast, and are very lean (call it agile) and efficient. One of their skills is to create and market products that automate a bunch of tasks, replacing lawyers, doctors, astronauts, other data scientists, and many others, by robots. Their original background can be nuclear physics, mechanical engineering, bioinformatics, Fintech, astronomy, or pretty much any domain that allowed them to get their hands dirty with various data processes, for many years.

Some also succeed by arbitraging systems (stock market, sport bets, click arbitraging on advertising platforms) and have no employee, no client, no boss, no salary, yet make a great income working from home. Once in a while, a full stack data scientist will accept an interview with your company, not to get the job, but solely to gain competitive intelligence and leverage the information learned during the interview, for instance to trade your company on the stock market. These data hackers know and play with data and numbers better than everyone else. They are very quick to find and identify information pipelines that can be turned in an opportunity. They also can play with various data sources and business models, and combine them to produce value. They shoot at targets that no one can see.

One example that comes to my mind is this: someone paying world leading experts to write the right content at the right time, that will be made available for free to the relevant audience, even smartly spending advertising money to heavily promote that content, yet refusing any commission from the money that the author could make from such promotion. The exact opposite of the traditional publishing model where in Europe, publishers want to sue Google rather than benefiting happily from the free promotion they get from it. Actually, they should pay Google instead, for having their articles freely disseminated. This example epitomizes what a full stack data scientist can do (on purpose, I did not mention how that guy makes money by paying authors and offering their content for free, but that is part oft he secret sauce.)  Would a publisher hire such a guy? No, they don’t even know that this guy makes their business model obsolete. 

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.

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