Three Myths about AI Jobs

What is the sexiest job of the 21st century? According to the Harvard Business Review, it’s being a Data Scientist. In 2021, it was deemed the 2nd Best Job in America according to Glassdoor research. (It previously held the #1 spot from 2016 to 2019.) Despite that, based on what we hear from our many customers and contacts, these jobs continue to have high churn. Why is that?

It is easy to read or hear about data science in the media and assume that associated roles will have that glamour. In 2019, Data Scientist was the highest-paying entry-level job in America. But, well-paying jobs may not necessarily provide experience that many seek, particularly early in your career. 

There are a number of reasons why data scientists often quit or change jobs: there is high demand, not enough work, and many companies are not ready. And as the AI space is relatively new, job definitions are not often strict. Lines blur between data engineering, data science, and ML operations. 

(To be clear: when we talk about AI in this article, we are talking about data science, machine learning, and AI.) 

I have had passion for and experience with AI and data science for over 15 years. Having spent the last 5 years running my own company and working with 40+ customers globally, I have seen first-hand how the field has evolved, and the accompanying confusion about what a job in this industry really entails. To clear things up, here are three misconceptions about data science and AI jobs – and what you can really expect from them. 

Myth #1: You will be working with a lot of data

The jump from theory to practice can be jarring for some, particularly those coming from academia. The reality is that you will have to learn how to approach real-world challenges. What people do not often realize is that having enough high quality data, in most businesses, is a challenge. It has been a problem on every project we have worked on. Often, there is a lot of high quality data but due to technical, accessibility, and legal challenges, you may not be able to use it for data science purposes. 

Myth #2: You will always work with the latest & greatest 

While the prospect of being able to work with the latest research or technologies is exciting, that chance will not often come to fruition. Many companies cannot afford that type of investment or are not ready for it. Most often, the newest tech is simply not needed or is too risky to deploy. 

Myth #3: It’s all about AI

People who start out in data science roles end up unhappy because they think their jobs will be all about data and AI. but the reality is being a data scientist demands more than that. You may find yourself building necessary pipelines and infrastructure prior to building models. Most businesses just don’t have that many AI problems – even larger ones – the work often ebbs and flows. At the end of the day, your job is to make an impact using math and data. 

So, what now? Do not be discouraged. Our tip? Be curious from very early on. Ask questions at the interview stage about data, feasibility, ethics, the list goes on: Will we have data? Is there a support system? How many others are on the team? Will things ship?

If you are already in an AI role, think about how to create new opportunities, measure the impact of your work, and remember: it is about the art of the possible – fall in love with the problem and be creative in your solutions. 

Don’t limit yourself or your role based on “buzzwords”. Ultimately, you will succeed by helping the business. Learn to manage your expectations when looking for a job that is the right fit for you. There are many businesses looking for these positions, so the demand is there. Companies like ours offer a unique opportunity because there is a lot of variety and diversity in the problems we solve, we believe in making an impact through continuous learning, and there are many opportunities for collaboration.

This article was originally written for and posted on Tech Talent Canada.

Photo by Sigmund on Unsplash

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