Founder and CEO Colaberry, Inc./Refactored.ai
Ram Dhan Yadav Katamaraja is the Founder and CEO of Colaberry, Inc./Refactored.ai. Ram is on a mission mission to reduce inequality by providing access to the skills, knowledge, and opportunities necessary for employment in today’s rapidly changing technological landscape. He believes in creating innovative skill development platforms that have inclusivity and equal opportunity at their core, and his technologies have directly assisted thousands of people from forty five countries, including veterans, minorities, women, refugees, immigrants, young adults, career transitioners, and even PhDs. The platforms that are being created by Ram have started gaining adoption by not-for-profit organizations like YearUp.
His Refactored.ai platform was selected for the MIT Solve global challenge in the Work of the Future category, as well as the winner of the General Motors Prize for Advanced Technologies and McGovern Foundation prize for Artificial Intelligence for the betterment of humanity.
Zoe Dobuler: What is your background, and how did you find your way to Refactored.ai? What problem does the platform set out to solve?
Ram Dhan Yadav Katamaraja: I’m an engineer by training, and then I did my Master’s in liberal arts in management from Harvard Extension School. And I’ve been working in the tech industry for 20+ years.
How we formed the path to Refactored.ai was really more of an epiphany: It wasn’t something we set out to start a business around. About seven years ago, I was looking to hire a military veteran as a data analyst to give back to the community, but I had trouble finding someone with the right skills. So, it ended up being that we had to train someone. We decided, “Ok, let’s train them, and figure out if we can train people for the jobs we want to hire them for.” That’s how it started. We started with an initial cohort of five people, trained them, and we ended up hiring three, and the two others found jobs elsewhere.
Through this process we realized that there’s a lot of demand for this type of work, and that there is a huge benefit for the families and communities of the people who are impacted. And this includes all demographics — not necessarily just veterans, but the initial cohort ended up including people from diverse backgrounds. It was growing so fast that we had to quit our jobs and go full time. We started this as a brick-and-mortar training company with an angle of “let’s do some projects and help people engage in those projects” type of thing, but after some time we realized that there is a lot of different techniques that need to be used to actually re-skill people.
Then we wanted to figure out how we could automate these techniques that were using to help people learn these new skills. How do you take a veteran and move them to a data analyst job? How do you take a barista and move them into another position? How do you take a customer service rep and move them into some sort of data science team? It requires not just hard skills, but a lot more things, too. And we ended up doing a lot of automation to scale this, and Refactored.ai is the platform we created. We use gamification methods, data science, artificial intelligence techniques, and all kinds of cool things to help us scale this and make it available to people all over.
ZD: It sounds like there is a huge demand, both among prospective employers and prospective employees — for this re-skilling.
RK: In the market, the job opportunities for people with data skills are exploding — there is huge demand. Literally, there are news reports about millions of jobs not being filled because of the lack of people with data skills. So, we fell in the sweet spot.
ZD: Can you tell me a bit about how the Refactored.ai platform works?
RM: To do our work we had to focus on retraining people in tech skills and human skills. In terms of tech skills, we chose data as the core skill. People typically pick coding as the core skill, but the beauty of data is that it’s like playing with Legos: You can see it, you can play around with it, you can fall in love with it.
And the other thing about the data industry is that the number one skill you need is domain knowledge — not tools, not coding. Those things are secondary. Having domain knowledge means someone understands the business they’re in, and that’s their biggest asset. And now when you teach data as a skill, they can immediately use data, combined with domain experience, to become really good. And then we introduce a bit of coding, and coding becomes almost an automatic skill because they’re just thinking, “Ok, how do we fix this data?”
Teaching human skills was one of the big challenges we had to work on. When you’re talking about human skills, you’re talking about dealing with fear of change, fear of the unknown, and learning time management, critical thinking. And we found some innovative ways of teaching them. We use a few pillars: One is basically “you can’t be what you can’t see.” So, we created a community of mentors, and these mentors are people who actually went through the program and succeeded. It’s not bringing in outside people, but people who are from the ecosystem. They went through the same path, so the newcomers have more trust and know that if they did it, I can too. It’s extremely powerful. It’s like when somebody comes to the platform, they come with the mindset of succeeding, because they’ve seen people like them succeed.
And the second pillar we use is you can learn all kinds of skills, but if you don’t know how to apply them, it’s useless. That’s where the work experience factor comes in. In the tech space, a lot of work gets done remotely. Million-dollar projects get done remotely. So, there’s no way you can’t create virtual internships, virtual projects, and guide people that way. So, we created this model where people can work virtually on projects and learn skills. Together, these two pillars have allowed us to help people change their mind that they could succeed, and also build portfolios from anywhere, anytime, and also being in sweet spot of data allowed us to succeed in what we do.
ZD: Does the Refactored.ai program help participants find employment after they graduate?
RK: For us, participants don’t graduate until they find a job. Our KPI is one interview equals one job. That means that a lot of times, the people we’re training don’t have an opportunity to do interviews. So, if they blow that one chance, getting a second shot will take a lot of time, and they’ll lose confidence. Now, the solution we came up with was measuring performance in terms of technical skills, implementation skills, and human skills to an extent that we can say with almost 100% certainty that if they get an interview, they’ll get the job after that first interview.
We’ve built a lot of technologies around this. Right now, about 1/3 of people get a job after their first interview, and we’re still continuing to improve that number. The other thing we do as part of helping them find jobs — given that we’re in the data industry — is to train people to just apply for jobs. On average, it takes 40+ submissions to get an interview, and there’s a lot of demand out there. So, there is some recruiter or hiring manager willing to give them a chance, and all we want to have happen is that when someone gets that chance, they get the job.
And when I say they don’t graduate until they get a job — because we are a tech platform, we don’t need physical spaces, so we don’t have to end the program at a certain point in time. We give them the skills, community, mentoring, communication skills, job experience, and then they’re in that part of the platform where they’re working with mentors, etc. until they get a job.
We also help them for a period of 12 months after they get a job. And we do that for another reason: Because the populations we are serving often cannot afford to take these classes or pay high fees (and we need to run a sustainable business) we introduced income sharing. Participants pay us over a period of 12 months after they get a job. So, we are incentivized to have people succeed! The Refactored.ai platform allows this whole ecosystem — the skilling, community, financial support, and job support—to operate at scale. At any given time we have about 500 people going through the platform, and probably every week one or two people get jobs, and it’s growing.
ZD: Is that what you would say sets Refactored.ai apart from other skills training programs?
RK: What sets us apart is one, we’re for-profit, and because of that we have to figure out how to create a business model to succeed. So, that forces us to innovate. And we figured out that data is easier to teach and also an easier skill for people to learn because it’s more tangible — there’s less abstract thinking required.
And the populations we serve already have a job — or two — so we offer part-time boot camps. They can do the instruction in-person at our facility in Dallas, or they can do it online, where we created a virtual studio where people can from join anywhere in the U.S. Then we have this platform that allows us to support hundreds, maybe even thousands — and hopefully millions of people in the future — at any given moment. And there’s no time constraint. For us, it’s not that we start now and end at a certain time. We start a new cohort almost every three to four weeks, so it’s more like Montessori method, continuously people are coming in and out. There’s a constant touch between everyone in the cycle.
And that’s allowed us to do a few really cool initiatives. One example is that we’re a data analytics partner for YearUp. They were trying to train data analyst interns to work at Facebook. So we were like yeah, let’s do it! And then Facebook gave us a curriculum, and I told them it wasn’t going to work. Because they wanted us to teach Python, SQL, and Tableau — a coding-first approach. And I said that’s not going to work; we need to teach them Excel first, then Tableau, then SQL, then Python. By the time they get to Python, they’re be like “Python is the easiest thing!” So that was one thing. Now more than 120 young adults that we trained work in Silicon Valley companies. From our research, almost 90% of them got full-time jobs in these companies because there’s so much demand.
ZD: Your company is based in Boston — why have you chosen to found and build Refactored.ai here?
RK: It was easy, because I live here! I got transported from Dallas to Boston in 2006. And when I first came here, people told me that I was never going to leave Boston because it’s my type of town. No, but I love this city, and the most important thing about Boston is that if something is working, we get nervous and we feel like we need to break it in order to make it better. So there’s a lot of innovation, there’s always a feeling that the status quo isn’t good enough. So if we came up with a big idea, using technology to develop communication skills, or using gamification methods, all these various things we’ve tried to experiment with — it’s because of the ecosystem that Boston provides, the whole innovation economy.
We do the innovation work here, and then the implementation work in Dallas. We also cater primarily to the South, Midwest, and Rust Belt markets, since that’s where most of the need is for these kinds of programs. Here you have a lot of resources, but in these areas there aren’t many resources. And given that we’re able to build an online ecosystem, it allows us to innovate here, then conquer the world!
ZD: From your perspective working in this space, how do you foresee the workforce developing over the next 10 or 50 years? What would you like to see the workforce of the future look like?
RK: That’s a trillion dollar question! Nobody is able to answer that. But from my viewpoint, the work of the future is now. There are two key things that people talk about in the work of the future. One is lifelong learning, and the other is human skills. Lifelong learning, for you and me is easy, but for people who are trying to shift, we need to teach them lifelong learning. Our platform is using data-first training to help people develop lifelong learning skills.
And the other is human skills. When we’re talking about learning human skills, it’s basically the story of my path. For the first 10 years of my professional life, I didn’t get a job in an interview. If someone gave me a job I did very well, but I couldn’t get jobs initially. What I figured out in my 30s, because of a lot of mentors I had, is that simple skills are required for somebody to become good at talking. And those are skills I worked into this platform. They aren’t complicated — they’re simple, simple techniques. So, developing this capability to provide these human skills at scale would be the most important thing for people to succeed in the future. We still have to figure out how it evolves, but now and to the next five years, almost every job requires data skills and data literacy, and almost every job requires human skills.
ZD: We’re meeting today at MIT Solve, since you were selected as a 2018 Work of the Future Solver. Having been a part of the program for the past several months, what have been your biggest takeaways from the experience?
RK: There’s a lot! I would say, when we got the mentors, that allowed us to think completely differently. They forced us to go back to the drawing board and figure out how we can think about scale. Because prior to this we had a business model, but MIT Solve gave us the confidence and validation that the work we’re doing is really important for the future of work. And now we had to go back to basics, and the mentors helped a lot with that.
Also, a lot of networking opportunities. We have so many impact investors interested in talking to us. We talked to the World Bank, the United Nations. I was a moderator on a panel at a conference at Harvard Business School. So, there is a lot of exposure, which has been really great.
And internally for our team of course it’s super exciting. Previously, it was like we know we’re working on something important, we’re changing lives. But now it’s validation that what we’re doing is a lot more important than we thought, it’s a realization that the entire world could need us.
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