Getting Paid to Get Your MBA in AI

Introduction

“It is my impression that the world of deep learning *research* is starting to plateau. What’s booming: deploying DL to real world problems.” Francois Chollet

Chollet is an author of the popular deep learning open source library Keras, and he works in the Deep Learning department at Google. I like this quote because in less than 140 characters he describes the monumental shift that is occurring in the way Artificial intelligence (AI), deep learning, and machine learning are rapidly moving from academic science projects into actually being used in the real world to improve outcomes and assist with more intelligent decision-making. At DataRobot, we sometimes refer to this trend as the pragmatic application of AI, and when these algorithms are successfully deployed to real-world problems, the results and value creation can be simply astounding.
Even more so, thanks to recent advancements in automation and rapidly declining costs of cloud computing, the development and deployment of machine learning algorithms have largely been commoditized. The name for this technological advancement is automated machine learning and it has absolutely changed the way data science will be disseminated, integrated, and implemented into the real world. It’s hard to believe, but I personally think that when the market reaches full maturity and peak adoption of automated machine learning has been realized, it will likely be viewed as one of the greatest technological advancements we’ve seen in modern times.

You no longer have to be even remotely proficient in coding or statistics to apply automated machine learning algorithms to your data.

But, this article is not specifically about automated machine learning, but the vast learning and career development opportunities it has created for intelligent individuals to combine their creativity, passion, and domain expertise with this new technology to generate vast amounts of tangible value not just in the business or enterprise world, but society as a whole. If that last part sounds like hyperbole, you can read about how folks with no prior technical or mathematical background have used this new technology to improve the outcomes for foster child placements, or improved the efficiency in how they are lending money to young African workers and entrepreneurs.
There are a number of ways to expose yourself to automated machine learning and the pragmatic application of artificial intelligence. One way is to become a trained end-user of automated machine learning software (like DataRobot) as highlighted by some examples cited above. You’d work on going very deep within a particular industry, first focusing on spotting new opportunities for machine learning within your specific organization or industry, and then turn your efforts towards gathering the historical data required to leverage automated machine learning and create new applications. The other way, as I will discuss below, would be to join a company that is at the forefront of this technology and immerse yourself in the variety of diverse problems they are solving using AML.

The Opportunity

Close your eyes (but keep them open or else you can’t read this) and imagine an academic institution with an “MBA in AI” program that offers:

• Technical professionals as professors, including four Data Scientists each of whom has previously ranked #1 in the world on Kaggle out of 300,000+ participants, and approximately 150 of the brightest minds on the planet working in the field of machine learning and artificial intelligence (AI).
• Business advisors that include some of the best investors and business executives in the technology sector — investors that have experience funding companies like Salesforce.com, Tableau, Box, Jet.com, 23andMe, and Vertex Pharmaceuticals and executives with experience helping grow enterprises like AIG, IBM, HP, Cloudera, Workday, and Traveler’s Insurance.
• A case study-centric curriculum focused on the pragmatic, real-world application of machine learning and AI in enterprises ranging from big (think the world’s largest banks, governments, insurance organizations, energy corporations, and life science organizations) to small (like a two-person non-profit doing micro-lending in Africa) and everything in between (think professional MLB teams and ‘unicorn’ startups).
• A perfect blend of business insight – How do we acquire new customers? How do we reduce fraud? How do we develop new products to compete with the technology giant that is quickly and ruthlessly entering our space? – with data science expertise –  What kind of data do we have today, and what can we acquire to properly frame this specific problem? Is there a way we can structure this unsupervised learning problem as supervised? Are we using variables exclusively known at the time of prediction?

This is not your ordinary MBA program.

Sound like a good investment? You haven’t heard the best part: tuition. It has the same kind of high-profile network your classic Ivy league programs offer, but believe it or not, this program actually pays you to attend. It does require hard work, but is that a bad thing if you’re constantly learning, being challenged, and being rewarded for what you put in?
If this sounds too good to be true, I can assure you that it is not. I can speak from experience being currently enrolled in getting my MBA in AI here at DataRobot – and I am joined by close to 100 ‘classmates’ who make up the rest of our Go-to-Market team. Every day, we solve complex problems across all industries and share that knowledge to help develop a technical skill set in non-technical individuals that many would argue is the equivalent years of academic study.
Each day we speak with dozens of current and prospective customers about the problems and challenges they are facing with growing their respective organizations using machine learning and remaining competitive in the global marketplace. Given the nature of what we do, the conversation quickly transitions to how they can monetize all of the data they have spent so much time and money collecting.

One of the best parts is that we get to do this for all kinds of industries and solve all kinds of different problems.

Curious how AI is being used in pre-clinical stage drug research? Just ask Matt Sanda, an Inside Sales rep on my team who spent six months working as a project manager with one of the world’s leading pharmaceutical companies before they eventually became our customer.
What about how AI is transforming the hedge fund industry and modeling on new sources of alternative data are changing the way even the most fundamental shops evaluate their investment theses? Rich McCormack, another sales rep here with no prior experience in quantitative finance, can tell you all about it. He worked with a few of the most respected firms on Wall Street to help them become power users of our platform for investment research.
Okay, I’ll end this shameless recruiting post with some good news and some bad news:
The good news: We are still accepting enrollments for 2018. We are hiring across the board to help DataRobot execute on the next stage of exciting growth that is ahead of us. Join us and help us build something special.
The bad news: I can’t speak for the company, but my team has only accepted 2% of the applicants( 6 out of 300) over the last six months. Statistically speaking, you have better odds of getting into Harvard Business School (12%).
More good news: If you are lucky enough to get into HBS, fear not! You will still have a chance to learn a lot of what I’ve outlined above. DataRobot has been integrated into some of the HBS curriculum to help democratize the ability to build machine learning models and focus on their pragmatic applications in the business world.
The bad news: If you do it that way, you won’t get paid for it.

About the Author:

Ben Solari is the Director of Inside Sales at DataRobot, where he now leads a team of 12 Corporate Account Executives spanning from Boston to Singapore. He started as the first Inside Sales hire at DataRobot in March 2016. His background is primarily in finance and analytics, with previous data and analysis roles at organizations like ESPN, UBS, Trillium Trading, and InsightSquared.