Hiring Where Talent Doesn’t Exist
How I built Pathao's team from scratch in a market where startup talent didn't exist - and why hunger always beats experience.
The first person I hired at Pathao was a seventeen-year-old kid I met at a party.
Not a startup networking event. Not a campus recruitment drive. An actual party (someone’s birthday, I think) where I was nursing a Coke in the corner and this kid walks up to me and says, “You’re that guy building the motorcycle app, right?”
I was that guy. Still am. But back then, being “that guy” meant something different. It meant you were either crazy or broke. Or both.
“Yeah,” I said. “Why?”
“I’ve been following what you’re doing. It’s brilliant. When can I start working with you?”
I almost laughed. Here’s this high school student, probably hasn’t even finished his A Levels, asking me for a job. But something about the way he said it, not desperate, not naive, just direct. Confident. Like he already knew something I didn’t.
“What can you do?” I asked.
“Whatever you need me to do.”
That was it. That was the interview.
Six months later, he was running product. Making decisions I wouldn’t have trusted to people twice his age. He didn’t wait for permission or job descriptions or org charts. He just saw problems and fixed them.
That’s when I learned the first rule of hiring in a place like Bangladesh: experience is overrated. Hunger isn’t.
The Experience Trap
In Silicon Valley, hiring is easy. Well, easier. Reid Hoffman hops from PayPal to LinkedIn. The Collison brothers leave MIT to build Stripe. Engineers move from Google to Facebook to Uber, carrying their playbooks like inherited wealth. The ecosystem feeds itself.
In Dhaka circa 2015? We had none of that.
No one had worked at a ride-sharing company because ride-sharing companies didn’t exist. No one had built real-time dispatch systems because no one needed real-time dispatch systems. No one understood growth metrics or user acquisition or marketplace dynamics because those weren’t problems anyone had tried to solve.
So when we did find people with “relevant” experience, they often worked against us.
The logistics guy from a traditional courier company would walk in with processes designed for a world where customers waited two weeks for delivery. Forms. Approvals. Physical receipts. He thought in terms of days and weeks. We needed people who thought in terms of seconds and minutes.
The software engineer from a bank would design systems assuming users had infinite patience. After all, banking software has never been about speed, it’s about not losing money. Our users would abandon the app if it took more than three taps to book a ride.
Experience, I realized, could be a liability. These professionals knew how to solve yesterday’s problems. We were trying to invent tomorrow’s solutions.
Finding Hunger in Strange Places
Instead of experience, we looked for something harder to quantify: hunger. Raw, unfiltered drive.
The best people we found weren’t posting on job boards. They were high school students who spent their evenings teaching themselves to code. University kids who showed up to startup meetups even though they had no business being there. People who sent cold messages on Facebook saying, “I don’t know what you do, but I want to help.”
Brian Chesky talks about how Airbnb’s early employees had to be “missionaries, not mercenaries.” Same principle, different context. We needed people who believed in what we were building, not people who just wanted a paycheck.
But finding them meant fighting cultural expectations. Parents wanted their kids in “stable” jobs at banks or multinationals. Friends questioned why anyone would join a motorcycle taxi company when they could work at Grameenphone or British American Tobacco.
The concept of equity was foreign. Stock options? Most people had never heard the term. We spent as much time explaining startup compensation as explaining the actual job.
Working Sessions Over Interviews
Traditional interviews were worse than useless, they were actively misleading. Someone could nail every behavioral question but crumble when faced with real ambiguity.
So we stopped interviewing and started working.
Give someone a project. See how they approach it. Do they ask smart questions? Do they come back with ideas you hadn’t considered? Do they take ownership of the outcome, or just complete the task?
Sometimes two hours told you everything. Sometimes it took two weeks. But working together revealed things no interview could: how they handled frustration, how they communicated under pressure, how they learned from mistakes.
Spotify does this with their “hack weeks”—they throw potential hires into actual projects with real teams. We couldn’t afford hack weeks, but we could afford small projects. Build a simple landing page. Analyze our customer support data. Figure out why driver signups are dropping in Uttara.
The seventeen-year-old from the party? His first project was calling fifty drivers to find out why they weren’t coming online during peak hours. He came back three days later with a spreadsheet, a presentation, and three specific recommendations that increased our active driver rate by 20%.
But I didn’t just throw him the task and hope for the best. I spent hours with that kid. Not just explaining what to ask drivers, but how we thought about problems, how we made decisions, how we treated people who depended on us for their livelihood.
It wasn’t charity. It was math. If I spent twelve hours training him properly, and that training improved his performance by even 1%, I’d gain hundreds of hours of better work over the next year. That’s a 20x return on time invested.
More importantly, I was the only one who could do it effectively. You can’t outsource the transmission of company culture and standards to someone who’s never lived them. The person doing the training has to be a credible role model, someone who’s actually solved the problems they’re teaching others to solve.
That’s when I knew he was different.
The Speed vs. Quality Trade-off
As we grew, every decision became a trade-off between doing things right and doing things fast. We almost always chose fast.
Building an automated compliance system for driver verification would take ten engineers three months. Training twenty people to process documents manually took two hours. Our backend wasn’t sophisticated software, it was an Excel sheet holding the fate of thousands of drivers.
We were a tech company solving problems like a call center. And for a while, that was exactly what we needed to be.
The alternative—building perfect systems while competitors ate our lunch—would have killed us. Uber was coming. Local competitors were launching. We had maybe six months to establish market presence before it became impossible.
Travis Kalanick used to say, “Uber is a math problem wrapped in a political problem wrapped in a human problem.” In Bangladesh, it was that times ten. We couldn’t afford to solve for elegance. We had to solve for survival.
The Mass Hiring Factory
By 2017, we weren’t hiring individuals—we were running a hiring factory. Twenty new people every week. I stopped recognizing faces in the elevator.
This is where all startup hiring advice breaks down. You can’t do careful cultural fit assessments when you’re hiring eighty people a month. You can’t have founders personally vet everyone when entire departments are materializing overnight.
Your early hires become your hiring filters. Your company culture becomes your screening mechanism. People who thrive in chaos self-select in. Those who need structure and predictability filter out naturally.
The challenge wasn’t just quantity—it was maintaining quality while scaling like a rocket. Some weeks we’d hire future leaders. Other weeks we’d hire people who lasted two weeks before realizing startup life wasn’t for them.
Manufacturing Teams from Scratch
When we realized we needed a data science team, Dhaka had maybe five people who could credibly call themselves data scientists. None of them were available.
So I went back to my university, found a statistics professor, and asked for help. “Can you build me a team?”
He walked in a week later with his four best students. They weren’t data scientists, but they were smart, curious, and eager to learn. They grew with us, evolving from solving academic problems to solving real-world problems at scale.
This became our template: identify the skills we needed, find the closest academic equivalent, convert students into professionals. It wasn’t efficient, but it worked.
Stripe did something similar in their early days, they hired people who were smart generalists rather than specialists. Patrick and John Collison figured it was easier to teach smart people about payments than to teach payments experts to be smart.
The LinkedIn Strategy
While hunting for employees, we hunted for mentors. Bangladesh might not have had startup veterans, but it had non-resident Bangladeshis who’d seen how things worked in mature markets.
LinkedIn became our secret weapon. I’d spend hours searching for Bangladeshis at tech companies in Silicon Valley, London, Singapore. The pitch was simple: “We’re building something unprecedented in Bangladesh. Can you help?”
But simple doesn’t mean easy. That outreach message took me twelve drafts to get right:
“Hi [Name], I’m [Your Name], founder of Pathao, Bangladesh’s first motorcycle ridesharing platform. I found your profile and saw you work at [Company] in [City]. We’re solving transportation problems for 20 million people in Dhaka, but we’re doing it without a playbook—because no one’s built this kind of business here before. I’d love 15 minutes of your time to get your perspective on [specific challenge related to their expertise]. We can’t pay consulting fees, but we can offer a small equity stake if you’re interested in helping build Bangladesh’s first unicorn. Would you be open to a brief call?”
The response rate was about 30%, way higher than I expected. But the conversations taught me something crucial: people wanted to help, but they wanted to know exactly how their specific expertise could make a difference. Generic asks for “advice” got ignored. Specific asks about marketplace dynamics, or payment processing, or growth metrics got responses.
Building an advisory network of NRBs gave us more than advice—it gave us credibility. When potential hires saw recognizable names associated with the company, it signaled we were serious.
Equity as Education
We were broke most of the time, which meant we couldn’t compete on salary. But we had something potentially more valuable: ownership.
Your equity is worthless until you have an exit, so why be stingy? We gave equity generously - to employees, advisors, even people who helped us for a few weeks during critical moments.
But first, we had to educate people about what equity meant. Most had never heard of stock options. The concept of owning a piece of a company’s future value was foreign.
This wasn’t just compensation; it was alignment. When someone owns a piece of the outcome, they think like owners, not employees. They care about long-term success, not just monthly salaries.
We gave equity to professors who helped us recruit students. To advisors who spent hours helping us avoid expensive mistakes. To the statistics teacher who built our first data science team. If someone could help us avoid a hundred-thousand-taka error, why wouldn’t we give them a piece of something that was currently worth zero but could become worth millions?
The People vs. Technology Paradox
Here’s the dirty secret: we had armies of people manually processing what should have been automated.
Our compliance team was entirely human-powered. Customer service was agents on phones, not chatbots. Our operations team used WhatsApp groups to coordinate with drivers because building a proper fleet management system would take months we didn’t have.
We were supposed to be a tech company, but we solved most problems the way companies solved them in 1980. And for a while, that was exactly the right choice.
Because in a market moving as fast as ours, with competition as fierce as ours, perfect is the enemy of good enough. The companies that survived weren’t the ones with the best technology, they were the ones that moved fastest.
Amazon started as a bookstore run out of Jeff Bezos’s garage. They didn’t build sophisticated algorithms on day one. They built a website that could sell books, then figured out the rest.
Creating an Ecosystem
We weren’t just filling positions, we were creating entire categories of professionals that didn’t exist before. Product managers who understood marketplace dynamics. Engineers who could build for real-time systems. Operations people who could think in terms of network effects.
By the time we reached scale, we’d directly employed around fifteen hundred people. But more importantly, we’d created a template. Other startups could now hire our alumni. We’d proven you could build world-class teams in markets without existing talent pools.
This is the long game of hiring where talent doesn’t exist. You’re not optimizing for immediate productivity, you’re betting on potential and growth trajectory. You’re creating the foundation for an entire ecosystem.
Some of those bets don’t pay off. People leave. People don’t develop as expected. People get overwhelmed by the pace of change. But the ones who stick become your foundation and proof of concept for everyone who comes after.
When the next wave of Bangladeshi startups started hiring, they could point to our alumni as examples of what was possible. Talent begets talent. Success stories inspire more success stories.
The Proof Is in the Results
Building without existing talent is harder short-term but potentially more rewarding long-term. You get to write the playbook. Set the standards. Create the culture that defines how things get done.
And you get to watch people transform. That seventeen-year-old from the party? He’s now running operations for a logistics company that’s three times the size Pathao ever was. The statistics students who became our data science team? Two of them started their own companies. One joined a Silicon Valley startup.
They didn’t just learn skills, they learned how to learn. How to adapt. How to build things that didn’t exist before.
That’s the real value of hiring for hunger over experience. Experience teaches you what’s been done. Hunger teaches you what’s possible.
Because in the end, talent isn’t found. Talent is created. And when you’re building where no one has built before, creation is your only option.