The Nedbank N*ovation Data & Analytics Masters, a national programme designed to identify and fast-track South Africa’s top data and engineering practitioners into a real hiring pathway, has concluded.
The challenge drew over 1 200 registrations, culminated in an invite-only finale at Nedbank’s headquarters in Johannesburg, and offered a prize pool of more than R500 000.
According to Nedbank, the investment in the Masters Data Challenge is rooted in its ambition to become a data-led, AI-enabled bank, critically due to the skills shortages and global demand of essential skills in the technology sector.
The programme focuses on homegrown talent by offering a workable, replicable model for large South African enterprises: invest deliberately in finding, growing and amplifying local practitioners using a performance-based mechanism that surfaces capability beyond traditional recruitment channels.
“The Masters Data Challenge is part of a broader, long-term effort to build a sustainable data and analytics organisation, rather than a once-off initiative,” says John Donnelly, group chief data and analytics officer at Nedbank. “In essence, this is about building the foundations first: data, platforms and talent, so that AI can scale meaningfully and responsibly.”
Francois van der Merwe, founder and CEO of event organiser Otinga.io, says: “Too many corporate conversations about ‘skills’ still end in importing capability or pushing critical work offshore. Nedbank did something more strategic: it put real stakes behind local capability. Real data, a serious prize pool, and a clear hiring pathway. For us, it means that an enterprise deciding that South African talent is world-class, and acting accordingly.”
The competition ran in two tracks: a Machine Learning/Data Science track hosted on Zindi, where participants predicted the next three months of customer transaction value using real anonymised Nedbank data; and a Data Engineering track hosted on Otinga’s platform, designed as an evolving, production-style pipeline challenge that tested maintainability, scalability and efficiency under changing requirements.
In total, 50 finalists were selected for the in-person finale, which included reverse-mentoring rotations with Nedbank data leaders, a ‘Rescue This Failing Project’ team challenge, and talks from Prenton Chetty from Nedbank, Lee-Anne James from Microsoft, and Barbara Fourie from OfferZen and Leonardo dos Passos from Cars.co.za. The format reinforced the programme’s positioning as capability-building rather than a once-off event. Top performers received letters on the day as part of the hiring pathway.
Jason Lu, vice-president marketplace at OfferZen, says the calibre of South Africa’s senior technology talent is strong and increasingly globally competitive, but that the definition of a world-class senior practitioner has shifted.
“Today, South African senior engineering and data practitioners are expected to go beyond writing code or building models. They’re increasingly involved in shaping architecture decisions, system design and product direction, particularly as AI tools take on more of the routine implementation work,” he says. “With AI now embedded in day-to-day workflows across most teams, senior engineers are spending more time on higher-order problem solving and less on repetitive coding tasks.”
Lu adds that performance-based challenges point to a more evidence-led approach to hiring. “Traditional recruitment that is reliant on keyword-heavy CVs and rigid background checks is no longer sufficient to identify top-tier talent in an AI-driven world. When a prestigious financial institution like Nedbank runs a hackathon like this one, it proves that the local hiring appetite is leaning heavily into verified technical capability, problem-solving under pressure and ability to collaborate.”
Megan Yates from Zindi said South African practitioners consistently rank among the strongest performers across the Zindi ecosystem.
“The Nedbank Transaction Volume Forecasting Challenge reinforced that strength. The calibre of competitors and solutions we saw was comparable to top-tier global competition standards, particularly in the way competitors combined deep machine learning expertise with practical, production-oriented thinking around forecasting, validation strategies, feature engineering and complex transactional banking data.”
Yates says challenges like this also reveal talent that conventional hiring can miss. “A challenge like this reveals applied capability in a way a CV simply cannot, because everyone starts with the same dataset and problem statement and is judged purely on their ability to solve the problem,” she said. “It allows employers to identify high-potential talent based on real-world skills, creativity and performance under pressure rather than just credentials or previous employers.”