The Geek Circle The Voice of Africa’s Tech Community
Precious had a clear plan: become like Ben Carson, the American neurosurgeon. She applied to study medicine at the University of Lagos, Obafemi Awolowo University, and the University of Ibadan. None of them took her.
OAU offered her zoology instead. She negotiated her way into a physiotherapy programme and found she actually enjoyed it. Then COVID-19 hit, and everything stopped.
She came home during the lockdown. By chance, she discovered Dr Stephen Odaibo, a Nigerian American doctor whose work explored how AI could be used in healthcare. She was hooked. Her brother mapped out a data science learning roadmap for her. Her ultimate goal was to become a machine learning engineer. With no laptop and unreliable internet, she started learning on her phone, making trips to a cyber cafe when she needed a proper screen.
As she got deeper into machine learning, she became increasingly certain that she would not return to the university. Repeated university strikes were not helping. “I got tired of the constant strike in the university,” she says. The deeper pull was the realisation that she could combine AI and medicine without a medical degree. She left the programme after two years and did not look back.
The long list of no’s before Shopify said yes
Getting into Shopify was not the plan. It was what was left after everything else fell through.
Precious had been trying to get out of Nigeria and into a proper computer science programme. She applied to universities in the UK and the US, pursuing scholarships to fund her education. Most did not respond. One American school offered a partial scholarship that was insufficient. She applied to Google. Rejected. Microsoft. Rejected.
A mentor who knew she wanted to leave her programme mentioned Shopify’s Dev Degree, a programme that combined a computer science degree at Carleton University with paid engineering placements inside the company. He had applied the year before and not gotten in. She applied anyway. The application alone was demanding, full of puzzles and problems to solve. She pushed through it, went through several rounds of interviews, and got in.
“It felt like God crowning all your efforts with just one single blessing,” she says. The programme structured her growth deliberately. The first eight months covered eight to ten programming languages and stacks. She then rotated through team placements: frontend, backend, and machine learning placement, where she built models for image and form generation. She did all of it while simultaneously completing her degree.
After graduating, she moved into a full-time role on the Agentic Evaluation team, assessing AI models from companies like Anthropic and OpenAI and integrating them into Shopify products. It is work without a syllabus. “There are no textbooks for what I do right now,” she says. “I have to read research papers as they are being published.” Shopify moves fast enough that a new model released today might be implemented tomorrow, which means she is always learning at the edge of what is currently known.
Start before you’re ready, and don’t wait for the big win
Precious believes wanting results too quickly hurts new techies. When the results don’t come, they stop.
Her own approach was different. “I wasn’t expecting something big. I was just showing up every day and picking up things I needed to pick up.” During the pandemic, that meant waking up, going to the hub with her brother, coming back home, and still being at her laptop trying to debug something while her mother was in the kitchen.
“These days I see people wanting to learn coding simply because they want money or they want a quick job. That was never my intention. I just wanted to learn and have fun while learning it.” Her view is that curiosity compounds in ways that chasing money does not. Each thing she learned opened doors to more opportunities, which opened even more after that.
Build for people, not for the algorithm
Precious wants more founders to ask themselves one question: Who are you building this for?
It sounds obvious, but she thinks most founders skip it. They start with the idea, or the technology, or the pitch, and treat the people the product is meant to serve as an afterthought. Her view is that the order has to be reversed. “Once you identify those people, when you feel what your users are feeling, you will know how to build right for them.”
She uses Shopify’s own origin as the clearest example she knows. The company’s CEO did not set out to build an e-commerce platform. He had a snowboard business and could not find a good way to sell online. He built a solution for himself, realised other small business owners had the same problem, and expanded from there. “It started because he was experiencing it as a user. It was a pain point to him.” That founder-as-user clarity, she argues, is what gave the company its direction and kept it honest.
The same principle applies, and becomes more urgent, when AI is involved in the building process. Precious works on AI evaluation at Shopify and understands better than most how capable these tools have become. But she is cautious about what gets lost when founders hand too much of the building process to a model. “An AI model can make a lot of assumptions about what people are facing. But that might not be the reality of things.” The barrier to shipping something has never been lower, but lowering the barrier does not mean the output is right. A model can build you a website with user data leaking through the front end. It will not know that is a problem unless a human is paying attention.
Her advice is to keep the why visible throughout the build, not just at the start. “Always go back to your why, why you started, and make sure every code you ship is actually targeting a specific problem in that bigger problem.”
Paying it forward
When Precious was trying to find her way from Lagos to a career in machine learning, she had people who helped her. A female mentor based in the UK was especially formative. “If she weren’t there, my life would have been much more brutal.” That experience left her with a clear sense of obligation. “I don’t want a younger person to struggle with something I have already been through, and I have the answer for.”
The way she gives back is practical and spread across several channels. She runs a YouTube channel where she documents what she is currently learning, sometimes recording a video straight after leaving a class because she knows someone somewhere might find it useful. When she comes across a resource worth sharing, she posts it publicly, including her own verdict on it after trying it herself. For people who reach out directly, she offers calls when the question is specific enough to be worth a real conversation. She cannot respond to everyone, and she is honest about that. But for those she does engage, the goal is simple: share what she knows so they do not have to figure it out the hard way.
She is also part of the ML Collective, a community managed by her brother, who is completing a PhD at Carnegie Mellon. The community supports African researchers to write, review, and publish papers, and sponsors trips to conferences to present their work. Last year, around fifteen people travelled to Rwanda to present research that the community had helped them develop.
Her view on what the ecosystem needs more broadly comes back to the same principle: start earlier. Tech communities and mentorship networks tend to reach people at university, but by then, she argues, many have already locked into paths that do not fit them. “We shouldn’t have exposure until we get to university. The exposure needs to start early on.” The ambition is straightforward: give young people enough context to choose deliberately, not by default.
On being the only one in the room
Three years into her time at Shopify, Precious has a clear-eyed view of what it means to be a black Nigerian woman at a major global tech company. She does not romanticise it, but she does not frame it as a burden either.
On gender, she sees slow but real progress. In a team of ten at Shopify, two might be women and eight men. Not balanced, she acknowledges, but meaningfully different from being the only one. “The male-female gap is closing up bit by bit.” She credits programmes like She Code Africa, which gave her an early footing and a community when she was still learning on her phone, as part of what is moving that needle.
The racial gap is a different story. Being the only black person on a team is still common, even when gender representation has improved. But her experience of it has been largely positive, and she is direct about why. “Being black has actually given me more opportunities than the other way around.” At Shopify, she has found that visibility cuts both ways: when you are one of the few, people notice you, and when you deliver, that visibility becomes an advantage. “People bank on what you can deliver, compared to your skin colour.” She acknowledges that microaggressions and bias are real and that other black professionals in other environments have experienced things she has not. But her own experience has been shaped more by the quality of her work than by her identity.
Her advice for others navigating similar spaces is the same philosophy she has applied throughout her career: know your craft deeply, show up consistently, and do not wait for conditions to be perfect before you perform. “So far, you are delivering on your job; nobody is going to mess with you”.
Precious is building toward
Clear about where she is headed, even if the details are still forming. Precious plans to pursue a PhD at the intersection of AI and medicine, with her eyes on programmes in medical physics, medical engineering, or clinical AI. The end goal is to build the kind of technology that ends up inside hospitals: the diagnostic tools, the imaging systems, the infrastructure that clinicians depend on daily. “I might become a clinical medical physicist,” she says, “someone who builds the technology they use in healthcare.” A faculty role and her own research lab are also part of the picture. “I want to do research for a very long time.”
