As AI briskly disrupts today’s world of goods and services, some people lose their jobs to the process of automation. Yet paradoxically, as we become increasingly data-driven, more and more opportunities in the data space are forming at the same time as other jobs are disappearing.On Episode 27 of AI at Work, we meet with Colaberry - the company whose mission is to become a bridge between the world of disappearing jobs and new opportunities in the data science space. Ram Katamaraja, Colaberry’s CEO and founder, and Pawan Nandakishore, Colaberry’s data scientist join us to share their company’s vision for reskilling and upscaling people who are losing jobs by training them in data science skills and helping them transition to data-based jobs.
Preparing the Workforce for the Future of Work
Ram started Colaberry in 2012. One of his objectives was to give back to the community by hiring a veteran into his team as a data scientist. As he started looking, Ram found it difficult to find the people with the right skills. The plan evolved - he put together a team of 5 veterans and trained them in foundational data science skills.
It seemed that his mission was complete - he hired not one, but three of the trained veterans. But this wasn’t the end - Ram realized that there were a lot of people who would benefit from the kind of training he had offered to the veterans.
There was a clear need for opportunities to learn skills relevant to the industry, and Colaberry chose data as the key skill that they could teach. “Unlike coding, which is like art work, and you need to have a lot of abstract thinking, data is very tangible,” Ram explains, which makes it a skill that people can grasp more quickly.
Colaberry’s training focuses on basic data skills - acquiring, cleansing, labelling, plotting, and visualizing data, using it to tell stories, and making data-driven decisions. Targeting frontline workers, they are fundamentally giving them skills - both for doing their jobs more efficiently as data-aware individuals and transitioning to high-value jobs.
The results? Clearly impressive. “People who were working as truck drivers are now working as data architects. We have veterans who were working as cable box operators now becoming IT managers. We have at-risk inner city youth working in Silicon Valley companies like Facebook, LinkedIn, and others of similar caliber as data analysts or data engineers after transitioning,” says Ram.
“We started with veterans, but right now we focus on everybody,” he shares, “In the future that’s getting disrupted by AI, for you to operate you need to have data skills. You need to understand how to work with data. Our goal is to create data literacy in individuals and across the organizations, so that we can prepare the workforce for the future of work.”
In addition to being an organization with an impressive mission for social good, Colaberry’s business model is engaged and their success depends on the success of their students. They charge people for training only after they’ve landed a job.
Training also goes beyond the hard skills. Ram points out that tackling a fear of technology is often a primary focus, something that has to be addressed before a deep dive into the data skills, as clients often don’t believe that they can work with the technology. Training is aimed at improving soft skills like problem solving, collaboration, and communication - all of which allow Colaberry’s users not just to transition into jobs, but also to succeed and thrive in their new roles.
AI helps to automate Colaberry’s own process. Students record their practice runs in preparation for interviews, and receive AI-driven feedback on how to improve. What used to take anywhere from 10 to 100 hours of one-on-one mentoring with each student and about 10 interviews, on average, for someone to get a job, is now close to nothing. Colaberry’s data is now able to tell very accurately when a person is ready to go talk to a hiring manager, saving time both for the candidate and interviewer.
Colaberry’s work fills an important niche - as more industries become disrupted by AI and jobs are displaced by automation, more and more people will need to be trained in skills demanded by the industry - and data skills are certainly in demand.
Already focused on the future of work, Ram and Pawan share some of their predictions for the future of AI with us. They foresee increasing specialization in data science roles, much like the specialization in roles that occurred after the initial omnibus of “webmaster,” perhaps something along the lines of a data scientist who focuses exclusively on data gathering, as one example. In addition, they predict that people will not only be excited about AI, but will be equipped to actually apply it on a day-to-day basis. Lastly, they hope that reinforcement learning and associated tools will mature more, helping to propel Colaberry’s training automation further.