The Dangers Of Hiring A Highly Academic Data Scientist

There is a common belief permeating the AI world, especially from outsiders’ perspective: That artificial intelligence is an extremely complicated subject akin to rocket science, which requires data scientists with PhDs and many years of ‘experience’ to understand.

I want to share brief stories to illustrate the dangers of hiring data scientists with high formal education:

I was at the Ai4 Retail 2019 conference in New York City, and one of the panelists was a data science consultant with a PhD from MIT with a decade of academic experience. He was bragging in public about how clients overpay him to do easy work due to their ignorance in AI/ML, believing it’s an uncanny subject only understood by ‘geniuses’ (his comments and smug grins were even captured in video). Be careful when hiring a data scientist like this, especially for consulting engagements (check out my blog post titled, “AI Fundamentals For Business Leaders” to familiarize yourself with the basics of AI).

Another danger is the potential lack of business understanding. I recently attended a talk on machine learning, and one of the speakers made a dangerous assumption for one of his clients. The assumption was that increasing the prices of their products would decrease sales and company value. He assumed customers would buy less due to increased prices, but this is a false assumption in general. There are many examples of companies that raised their prices and their sales increased drastically. For some understanding of why this may happen, it’s because a cheap product tends to be perceived as less valuable, while a more expensive product tends to be perceived as more valuable. The ideal customers of some companies do respond better to higher prices, and sales take off. Be careful when hiring someone highly technical with a poor understanding of your business’ dynamics.

More recently, I met with a company that connects AI/ML consultants to enterprise clients. It’s a brilliant idea, and they have partnerships in place to make it happen. However, they made one of the biggest mistakes for their company. They hired a university professor with decades of academic experience and zero business understanding to screen the AI/ML talent before meeting the business clients.

Why is this a big mistake? Because he is screening people based on what is important to HIM (which is linear and purely technical), and NOT based on what clients want/need. In fact, when inquiring about clients’ needs, he would disregard the questions saying that doesn’t matter – that all that matters is the “many years of experience” using certain technologies.

Imagine you are the client, and there is someone like this standing between your current situation and your desired business outcome using AI/ML. How many opportunities to solve your problem are you missing due to this bottleneck? And, how do you feel about this technical academic person not caring about your needs? Would you hire this person to help take your company to the next level?

Obviously, there are a number of data scientists with PhDs who are successful and effective on the job, but a close analysis reveals that it has little to do with their formal education. They don’t assume they know everything, and they constantly learn how to better meet their company’s needs.

Real-life business problems are often unique and diverse. What we already know is rarely enough to help a business succeed. Continuous learning and resourcefulness are two of the most important elements for any AI/ML engineer to succeed (check out my blog post titled, “Top 3 Traits To Look For When Hiring AI Talent” for more information).

If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at and I will be happy to help you.

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