A Creative Solution to the Data Scientist Shortfall

Thanks to the ramp up of analytics, there is a massive shortfall in data scientists. The problem is not only the lack of folks with this skill set, it is the fact that the managers trying to find people to hire aren’t skilled in what to look for. Even if you outsource, making sure the firm, contractors, or service you hire can do the job and is creating what you want is a huge problem.

I may have accidentally run into the answer—a little firm called Experfy.

Given the critical nature of analytics in terms of both the value and the cost of this class of solution, I think spending a little time making sure you have the right people on the effort is worth your time.

Experfy: The Uber of Data Scientists

Experfy is a fascinating company. They’ve collected about 1,200 qualified data scientists into an Uber-like service (costs average about $150 an hour) broken down into four groups.

Group one are folks that are freelancing full time. They don’t really want to work for any one company and really enjoy moving from project to project with lots of free time in between.

Group two is made up of folks that have real jobs but like to moonlight. They may work for Google or some other Silicon Valley giant but have the time and want the extra income and experience of something different.

Group three is made up of academics that want to practice what they teach. This is not only good for their income, it gives them knowledge of what companies are looking for that they can build into their curriculum.

The last group is made up of small shops that can staff these projects themselves. This is handy because these small shops have teams that already know how to work with each other and have a resume of finished projects.

Now while the primary purpose is to select these people for project work, they can also help you find and interview talent to make sure the folks you get can actually do the job you want done. They can oversee vendors who are doing data analytics projects for you to assure these projects meet expectations, come in on time and don’t blow out their budgets because of miscommunications or bad staffing decisions. And they can serve as a sounding board for new projects to make sure they have been scoped, funded and staffed properly and are more likely to meet the objectives.

Applied Analytics: Assuring Staffing Quality

At the heart of Experfy is an employee qualification process that is built on analytics. In fact this may be the most interesting technical part of the company. They’ve written an incredibly powerful analytics-based prospective employee evaluation system targeting data scientists. This system scans all of the available information on a potential candidate and looks for problems, inconsistencies, falsehoods or issues that point to problems that otherwise might not have surfaced.

In this way the data scientists that they have in their pool are likely better vetted than most of the data scientists that are currently involved in huge projects in many of the technology firms. At its core it is this vetting process that assures the result of the projects that Experfy engages in.

Wrapping Up

There is a massive shortage of data scientists in the market and a lack of qualified people to vet hiring candidates. Experfy is a unique service that fills a critical need in the market by providing teams that can define, scope and execute projects and the skills needed to staff teams like this. They are, to my knowledge, unique in the segment, providing the most critical solution to any analytics project: assuring that the folks around it know what they are doing. Something we often forget to truly assure.

Photo courtesy of Shutterstock.

RELATED NEWS AND ANALYSIS

• Huawei’s AI Update: Things Are Moving Faster Than We Think

  FEATURE |  By Rob Enderle,
  December 04, 2020

• Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 18, 2020

• Key Trends in Chatbots and RPA

  FEATURE |  By Guest Author,
  November 10, 2020

• Top 10 AIOps Companies

  FEATURE |  By Samuel Greengard,
  November 05, 2020

• What is Text Analysis?

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 02, 2020

• How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 29, 2020

• Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 23, 2020

• The Super Moderator, or How IBM Project Debater Could Save Social Media

  FEATURE |  By Rob Enderle,
  October 16, 2020

• Top 10 Chatbot Platforms

  FEATURE |  By Cynthia Harvey,
  October 07, 2020

• Finding a Career Path in AI

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  October 05, 2020

• CIOs Discuss the Promise of AI and Data Science

  FEATURE |  By Guest Author,
  September 25, 2020

• Microsoft Is Building An AI Product That Could Predict The Future

  FEATURE |  By Rob Enderle,
  September 25, 2020

• Top 10 Machine Learning Companies 2020

  FEATURE |  By Cynthia Harvey,
  September 22, 2020

• NVIDIA and ARM: Massively Changing The AI Landscape

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  September 18, 2020

• Continuous Intelligence: Expert Discussion [Video and Podcast]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 14, 2020

• Artificial Intelligence: Governance and Ethics [Video]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 13, 2020

• IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI

  FEATURE |  By Rob Enderle,
  September 11, 2020

• Artificial Intelligence: Perception vs. Reality

  FEATURE |  By James Maguire,
  September 09, 2020

• Anticipating The Coming Wave Of AI Enhanced PCs

  FEATURE |  By Rob Enderle,
  September 05, 2020

• The Critical Nature Of IBM’s NLP (Natural Language Processing) Effort

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  August 14, 2020