• Nxt Level Recruiting Team

Machine Learning Hiring is at an All Time High.

Ever wondered how Amazon or even Netflix keep giving you recommendations? Contrary to popular belief, they don’t work for the evil forces of Skynet. And, it’s not likely you’ll be running into the terminator anytime soon.

Instead, they rely on one of the most popular Artificial Intelligence applications — Machine Learning. With technology advancing, the demand for machine learning and data analytics is at an all-time high.

There are barely any industries left that haven’t incorporated Machine Learning in their trade. And that’s not surprising because ML algorithms have a stunning range of applications.

From allowing computers to communicate with humans, to writing and publishing sports reports, there is nothing that Machine Learning can’t improve. Just look at the way it’s evolved over the years.

  • 1950: Alan Turing created the “Turing Test” to determine if computers could truly “outsmart” and fool a human being.

  • 1952: The first computer learning program was invented by Arthur Samuel with an IBM computer studying and evolving the game of checkers.

We skip a few years to:

  • 2006: The term, “deep learning” came to life when Geoffrey Hinton explained new algorithms that let computers “see” and distinguish objects and text in images and videos.

  • 2012: Google’s X Lab became the ultimate cat stalker by crawling YouTube Videos to identify videos that contained cat videos.

  • 2015 and Beyond: Stephen Hawking, Elon Musk, Steve Wozniak, Mark Zuckerberg, and more are battling the social implications of what integrity and trust really mean as Artificial Intelligence gets more sophisticated with the level of data available on the open web. We have robots, autonomous cars, auto-dialing phone calls that can verbally communicate, smart IoT services controlling our home, and Alexa asking us questions out of the blue while cooking dinner.

The other day, my 13-year-old sister asked me, “Why do I need a driver’s license? That’s a waste of time and money. By the time I can drive, cars will drive on their own.” Not only was I stunned to realize that she may be right, but I was also somewhat envious because I’ll never get the time or money that I wasted learning how to drive.

Now, more than ever, data has become the new “oil industry” and “gold rush” as we attempt to apply data into consumable and productionizable products. This is truly where Machine Learning comes into play.

Machine Learning has the opportunity to change a lot of industries in a very positive way! 

Forbes has suggested that machine learning has the potential to create an additional $2.6 trillion (in value) in Marketing and Sales by 2020. That’s not counting the $2 trillion in manufacturing.

And, if that isn’t enough reason to motivate, there’s the ever-growing market and volume of data. Companies are always searching for cheaper and powerful ways of computational processing, not to mention cost-effective data storage.

Businesses can double, if not triple their growth by analyzing data and building precise models. Machine Learning has a tremendous capacity to help an organization in identifying lucrative opportunities and avoiding risks.

Here’s what Anirban Sengupta, Head of Demand Optimization at Microsoft, has to say about the importance of Machine Learning.

“…However, as my career progressed in Amazon, the kind of problems I was exposed to, machine learning was the more obvious approach compared to traditional statistical and econometric modeling. For example, detecting fraud in real time is hard using parametric statistical models. The number of features is huge and there are interactions between them.
Also, in these cases prediction accuracy/precision/recall are more important than model explainability which makes ML a more preferred choice.
ML is here to stay. Investments in data are increasing and will continue to do so. The goal of these investments in data and data infrastructure is to mine these data to extract actionable insights from them to drive business value. The need for people who can actually do such complex data analyses and model building at scale is here to stay.”

Let’s consider how Machine Learning is impacting multiple industries: 


Think about the innovations that Machine Learning can make possible in the healthcare industry. As it is, Machine Learning is a growing trend in healthcare and has resulted in some of the most fascinating applications.


Located in Massachusetts, PathAI is coming up with technology with the help of Machine Learning that will help pathologists make faster diagnoses.

That may not sound like a big deal, but it can cut across hospital backlogs and allow patients to receive their treatments all the quicker. In diseases like cancer, early detection can make the difference between life and death.

Energy Sector

Environmentalists have been demanding cleaner sources of energy for years now. Not to mention, every time we turn around, there is another gas crisis looming on the horizon.

Oil and gas is another field where Machine Learning has endless possibilities. From creating more efficient refineries to analyzing minerals, there’s so much Machine Learning can be applied to.


Enverus provides gas and oil data that can improve drilling operations by developing resource plays built on multivariate models.

Not only do they help companies by rendering precise geological and geophysical data, but they can also reduce the amount of financial and human resources that a company extends in oil operations.

Financial Industry

As we shift to an increasingly online way of conducting transactions, banks and other businesses in the financial sector, have a greater need for the real-time data-analysis that Machine Learning can provide.

Cyber surveillance and fraud prevention applications can provide investors the surety they need to conduct their trade with ease.

IdentityMind Global

IdentityMind Global is an AI company that helps financial institutions and businesses combat online frauds.

The company has more than 50 established data points that help verify a person’s identity. Additionally, they also provide services that deter money laundering and counter-terrorism financing.

Want to get involved? Let’s talk Fundamental Machine Learning Skills.

Machine Learning is an evolving field, hence the fundamental skills that are requisite range across a broad spectrum.

Still, a strong academic background with vast experience works as a bonus. The predominant skills for ML are statistics, probability, programming, and data modeling.

And that’s exactly what Jia Chen, the Managing Partner at Softmax Data thinks too. When asked what skills a Machine Learning Engineer should possess, Mr. Chen said the following

“…Data Engineering, Software Development, Mathematical Skills, and Communication Skills.”

To understand the significance of data modeling in ML take a look at what Lee Sherry, Head of Data Science & Analytics at Geocaching says.

“…While programming experience is useful, I think modeling is very important: an ML engineer should know how to productively approach a problem. This means identifying a situation’s salient features, figuring out how to frame a question that will yield the desired answer, deciding what approximations make sense, and knowing which algorithms and methods to apply to the problem at hand.”

That’s not all there is to it. Structured data such as numbers, dates, and strings can be easily stored as rows and columns. Unstructured data such as videos, images, and e-mails cannot be easily designated. Read Igneous System’s article about Structured Data VS. Unstructured Data.

However, unstructured data is the basis upon which data modeling stands. It makes 80% of enterprise data, according to Gartner. Using unstructured data in machine learning algorithms provides vital insights. It has great application possibilities in business operation strategies.

It can improve accuracy, encourage new ways of thinking, and using information.

Finally, a thorough knowledge of supervised learning and unsupervised learning is also a must. These are two of the most popular machine learning methods. Let’s start with supervised learning. It’s a system in which input and output variables are available, and you typically use an algorithm to determine the mapping function from the data.

The goal is to come up with a precise mapping function so that the algorithm can predict the outcome when you enter new input.

If the technical jargon is getting a little heavy, think about it like this — supervised learning is valuable, where data plays a vital role in predicting future events. An excellent example is how financial institutions use supervised learning to predict which credit card transaction may be fraudulent.

On the other hand, unsupervised learning functions more like a training set. The system is given input data, but no comparable output values. The aim is to explore the data and come up with findings (think Sherlock Holmes).

Unsupervised learning works very well in fields like marketing, where it can help identify patterns of customer behavior. A business can then use these patterns to build a marketing strategy that’s almost scary in its accuracy (looking at you, Facebook).

Demand for Talent is increasing! F-O-M-O is getting real for a lot of engineers.

Like I said at the beginning, Machine Learning isn’t going out of style any time soon. In fact, many job search sites saw a jump of 90% in the number of job postings related to ML.

Except, there’s a catch — at least 40% of those job postings were still vacant after two whole months. This leads to the reasonable conclusion that though there’s a great demand for Machine Learning Engineers, it’s the supply part, that’s causing issues.

There’s also some debate among engineers about generalization versus specialization. But, here’s what you need to know — most of the industry-leaders we interviewed preferred their candidates to have a breadth of experience as opposed to just one field of expertise.

Most of the Managers, Directors, and CTOs that we interviewed made it very clear that they wanted people willing to apply their Machine Learning experience into a variety of projects without being fixated on their personal domain. If you specialize in object recognition and computer vision, don’t turn down an NLP project because it’s slightly different. Most ML leaders believe classical techniques are transferable between projects. 

Companies may want their employees to transition once in a while, but that’s not always the case. Though, generally, it won’t hurt your chances if you gain some experience beyond your domain.

Some trending fields of specialization include recommendation systems (utilized by tech giants such as Facebook), computer and machine vision (employed by the Snap, Inc Team), and Natural Language Processing (Apple’s Siri).

You don’t have to take my word for it. Instead, listen to what Alex Ermolaev — Director of AI at Change Healthcare says.

“…I believe it helps to focus on one field at a time, but still have some fun by moving between different fields every few years. It is good to focus on one field at a time because it does take some effort to be good at it, master the tools and learn the nuances…”

Companies are looking for candidates who excel at what they do, and are excited about new challenges. Taking on new challenges and understanding different domains within your organization will allow for you to cross-collaborate on different projects. This will ultimately give you more insight into multiple projects and understanding core users and customers. 

Therefore, if you’ve decided to break into the ML industry, that’s definitely a step in the right direction. It’s a rapidly developing field with a never-ending array of applications. As a matter of fact, Google Trends reports that Machine Learning is about to take over AI in search results.

The important thing is not to get bogged down in too many details. Yes, it’s a highly-competitive market, and yes, everyone wants a piece of the action — but eventually, it’s the standard of your work that’ll count.

Focus your energies on gaining experience across domains so that your willingness to learn communicates itself. Hone your mind, look at the world in terms of statistics, and come up with inventive solutions that’ll benefit you, your employer, or even society at large.

NOTE: In our next article, we’re going to uncover what Machine Learning Leaders are looking for when they are hiring and the common positive and negative trends they see candidates make in the interview process. 

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