In Search of Data Science Talent with Dr. Kirk Borne

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We have huge amounts of data, almost unlimited cloud computing, and constantly improving machine learning algorithms. So what's stopping companies from being successful with big data? "Talent, talent, talent," says Dr. Kirk Borne. "The limiting factor is talent."

Of course, Borne has done more than most when it comes to nurturing data science talent. Fourteen years ago, before joining Booz Allen Hamilton or joining DataPrime, Borne helped set up the nation's first data science degree at George Mason University.

This proved to be a pivotal point for data science in academia, and today there are thousands of undergraduate, graduate, and PhD degrees in data science across the country, not to mention an untold number of bootcamps and certificate programs.

With all efforts to shape new data scientists, the world should now be inundated with unicorns. However, the gap in data science remains. According to Borne, it all boils down to insatiable demand.

"The talent pool is growing almost exponentially," Borne told Datanami. “But unfortunately for the business world, so to speak, the number of job opportunities that companies create is also growing exponentially, but faster than promoting talent. The difference between two exponential functions is effectively still an exponential, and so there is still this rapidly growing talent gap. "

Rapid changes, changing skills

We have come a long way from the dawn of big data, says Borne.

Dr. Kirk Borne

“The technologies we use today are not what we used eight years ago,” he says. "Remember, Hadoop was all the rage and everyone had to learn Hadoop and hardly anyone mentions that word in one sentence anymore." (Well, almost no one!)

The Hadoop experiment was certainly painful for some. It's not exactly clear if we had to go through it (a good case can be made for we did it). In any case, it is important now that big data technology is much better and more usable today than it was 10 or even five years ago, and that is a huge benefit for companies that want to work with big data.

" is no longer an obstacle. It's the enabler, ”says Borne. “What is happening now is that we are in this phase of the platform revolution where you basically have almost unlimited scalability with the cloud. You don't have to buy your own supercomputer - you just rent it for minutes, hours or days when you need it and then give it back. "

Organizations today have a wide variety of compelling big data tools and AI technologies to choose from, most of which run in the cloud. The big three - AWS, Microsoft Azure, and Google Cloud - not to mention upstarts like Snowflake and Databricks and the hundreds of other companies in this dynamic ecosystem, are all involved in the rise of a "function as a service" that has dramatically impacted Access to Big -Data technology opened.

“Do you have to build a recommendation engine, for example, or do you need a chat bot?” Says Borne. “You're basically just calling up this feature that someone else has already built. Why build it yourself, for example? "

The accumulation of ready-made functions and ready-to-use data science platforms in the cloud opens up many new business opportunities. Two kids in a garage doing huge jobs processing data with SQL analytics or training a machine learning model with the latest data can now control it from a single console with an API call. They are now competing with multi-billion dollar multinationals. It lowered the bar - and raised the stakes for everyone.

“The platform revolution has made plug and play possible for all kinds of different applications, tools and services,” says Borne. “You just put them together to serve a business community and you're basically on your way to the races.

Right, but what about the talent?

Unfortunately, amid the compelling wealth of cloud-based big data technology, the limiting factor is the persistent talent gap.

Signs of the talent gap are popping up everywhere. It appears in more than 11,000 data scientist listings on Glassdoor and more than 15,000 on Indeed. It shows up on urgent job advertisements for increasingly desperate recruiters, the median data scientist salary of $ 130,000 (as reported by Burtch Works last year), and data scientists every 2.5-2.8 Change jobs years (also per Burtch Works around 2019).

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All of this points to an ongoing seller market for data scientists - or whatever you want to call the technicians who get their hands dirty with data and machine learning algorithms (LinkedIn's top job last year was "AI specialist," while others prefer machine learning ). Engineers or scientists).

This is great news, of course, if you happen to be a data scientist. Then everyone wants you! You are a rock star! But if you happen to fall on the demand side of that equation - well, bad luck, buddy.

"If a company is just putting technology together and not understanding the science of A / B testing or the science of modeling customer behavior and all of those things, then you really get stunned," says Borne. “You still need people who understand the benefits, risks, and appropriate business uses of these things, not just the technologies and programming skills. And so the talent gap is really the fundamental hurdle at the moment. "

Prepared for data science

When Borne left BAH at the beginning of the year, he thought of retiring. But that didn't last long when he signed on as Chief Science Officer at the new AI startup DataPrime.

The goal of DataPrime is to connect data science professionals with potential jobs by using - you guessed it - data science techniques. That's why they brought Borne in to help develop and implement this platform.

"It's basically a recommendation engine," Borne says of DataPrime. "In principle, we recommend employers to employ applicants for employees."

DataPrime is now accepting profiles of potential candidates. Individuals in all data careers looking for jobs can upload their résumé including skills, interests, experience, education, wants, preferences, and requirements. Companies looking for data scientists can also register and enter their requirements and job descriptions. The data science magic that Borne is helping to create will then attempt to balance the job experience and skills of the candidates with the needs of the company. DataPrime aims to provide hyper-personalization in talent discovery for both data professionals and recruiters in these professions.

However, not all data scientists are created equal, and not all data science positions are created equal. The interesting thing about DataPrime will be its ability to understand the nuances of a particular job posting and find a suitable candidate.

The best candidate may not always be obvious, says Borne. “I was on a panel and one of the members of the panel was the director of cancer research at a university on the west coast,” he recalls. “She said the best attitude she ever had about her research lab wasn't a cancer research scientist or a lab technician. The best job she's ever done is an artist, she said.

The artist's creativity helped bring new ideas and experiences to the technicians who designed cancer treatments and cancer programs for people. That's not to say that artists are recommended for data science jobs. But it does give you a glimpse into the non-intuitive way the company will read resumes, work experiences, and life experiences.

Borne encourages anyone with anything connected to data, even remotely, to try DataPrime, “whether cloud engineer, machine learning researcher, business intelligence, dashboard builder, data storyteller, database engineer - anything with that Word data analysis or AI everywhere in his job title - we want your job profile on our platform and so do the recruiters, ”he says.

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