Crunching Code, Breaking Molds: Zindi’s Jessica Randall Rewrites Data Vision

South Africa – 27 June, 2025 – Long before she had a name for it, Jessica Randall was already doing the work of a data scientist. She was breaking apart complex problems, reading the world through patterns, and reconstructing meaning from chaos. When she formally encountered the field during university, she knew her approach would be anything but typical.

“I do data science, but I do it from a very mathematical perspective… not so much statistical tools. So it’s like a different view into data science, which is quite different. It’s interesting to see how Maths looks at data,” she said.

That Mathematical lens defines how Jessica engages with everything from climate challenges to algorithm design. To her, data science isn’t merely about crunching numbers; it’s about seeing in ways others can’t.

Jessica’s inclination toward Mathematics began early, shaped by a deep curiosity about how things worked and a desire to solve real-world problems. It’s a mindset that has carried her through the more demanding aspects of her field, particularly when it comes to translating complex ideas for broader audiences.

“One of the hardest skills is to translate your language into something that someone else understands,” she said. Her go-to strategy? “Try to explain it to a five-year-old. And it works.”

How She Discovered Zindi

Jessica’s journey took a pivotal turn during the Deep Learning Indaba, a pan-African conference on AI. While attending sessions on machine learning and analytics, she heard a talk from a representative of Zindi that captivated her. That was how she joined the platform.

“When you think of software development, everyone says you have a portfolio and you have a GitHub. It’s kind of hard to showcase that in data science. So Zindi is the perfect platform to showcase that. It’s got everything from like challenges, different data sets… It’s kind of a place for everything data science.”

Not long after, she became a Zindi Ambassador, a role that combines technical advocacy, community support, and digital influence. From running Microsoft-backed workshops to reviewing the Databricks environment, she describes it as “influencer/representing Zindi/being there for the community. It’s basically like… the equivalent of a developer advocate, but for data science.”

On the Ground with Big Data

As she’s grown into her ambassador role, one area that continues to fascinate Jessica is the intersection of data science and climate science.

“Anything to do with the weather and climate change… that’s kind of very hard to predict,” she said. Despite decades of data, the elements remain elusive. “You can’t tell what the strength of a cold front would bring. Could be worse than last year, better than last year.”

This sense of unpredictability, far from discouraging her, energises her commitment to using data to support fragile systems, especially in contexts like agriculture and infrastructure, where African countries are often most vulnerable.

Her Perspective on AI and the African Context

Jessica is no stranger to the wider conversation about artificial intelligence, and she speaks about it with a mix of hope and strategic concern. “I think the problem with it is like, we don’t own the AI that we’re using. We’re borrowing it from some other countries, which means you must fit in with what they give us to use it.”

For Jessica, the path forward must involve local ownership and context-driven applications. She also envisions AI not just as a tool for efficiency, but as a creative partner that helps Africa amplify its strengths and solve persistent challenges.

Looking Ahead…

Now, Jessica’s goals entail expanding beyond solving problems with data. She wants to shape the ethical and regulatory frameworks that define how AI is used. “I really want to get into AI policy,” she said “I would best represent it in the sense that I use AI myself… I could relate to what [developers] need from AI and what they’re not getting.”

Her technical aspirations remain intact as she’s particularly drawn to improving data quality for model training.