Axiom Maths welcomed two brilliant Data and Impact interns this summer. Sharon and Billy joined to help with several projects, including an impact evaluation of two year’s worth of data to shape our future strategy. Over eight weeks, the pair immersed themselves quickly and reflected thoughtfully about their technical development and personal growth.
Here’s what they had to say:
How would you sum up your experience at Axiom Maths?
“If I were to model my eight weeks at Axiom Maths as a function, it would be a piecewise one—continuous in enthusiasm, but with occasional disruptions when life (or a tube strike) intervened. The three axes in which my function is plotted would be personal discovery, professional growth, and work-life balance.”
“I arrived as a data-curious intern who loves insightful datasets and interesting stories. By the end, I’d learned that the magic happens where the two meet – when numbers sharpen decisions and young people’s voices give them heart. Along the way I analysed mentor surveys, dug into NPS and outcomes, visited festivals and camps, and helped shape practical recommendations we can actually ship.”
What personal insights did you gain?
“Early on, I discovered a truth about myself: before I can construct a well-posed plan, I prefer to linger in exploration. Immersing myself in reading and learning about the organisation helped me use Python and Power BI confidently. In Weeks 3–4, I moved into exploratory analysis, including correlation and regression analysis. During the team’s annual meeting, this preparation allowed me to respond confidently to questions. This confirmed my tendency: I thrive when allowed to ponder first, then formalise.”
“Personally, I grew from analyst to advocate. I’m leaving with sharper data-driven instincts, more confidence in shaping recommendations, and a clearer career compass pointing toward data for social good. Education is complex, but the signal is clear: when we listen hard, measure honestly, and design for inclusion, more young mathematicians see themselves in the future. That’s the kind of dataset I want to keep improving.”
What new skills or discoveries are you proud of?
“From Python automation to learning DAX and M code in Power BI, I can now perform all the core functions I need, despite being a beginner just weeks earlier. Week 3’s visualisations felt like solving a neat proof, while Week 4’s regression analysis reminded me that data can be as unpredictable as London’s weather. Another major step was taking real ownership of projects. In the first Insights & Actions task, we were given directions but freedom to construct the analysis. We combined mathematical reasoning with technological tools into something rigorous and elegant. ”
“Impact shows up in behaviour. Across the year, mentors consistently described pupils being more adventurous, accepting and articulate—the “AAA” gains that make maths feel like a team sport rather than a test. That gave me permission to be practical: bank what already works in school settings and carry it into online and partner contexts. I also worked on the Junior Mentor pilot: sixth formers supporting maths circles. Feedback from pupils and mentors was overwhelmingly positive. It proved that relatable near-peer role models can unlock participation at scale without losing warmth.”
What moments will you remember the most?
“There were also lovely detours: Summer camp in Week 5 showed the organisation’s passion for students, while Week 6’s team meetings proved productivity can come from paper planes, quizzes, and fun.”
“On the ground, festivals and camps put faces to the figures. At the London Rising Mathematician Festival, one pupil told us, ‘I originally wanted to do music or design… maybe I want to do something that changes the world a little bit.’ I won’t lie: I got goosebumps. After, I suggested organising interactive games in our online circles to encourage peer interaction and deepen friendships—key to improving retention.”
We’re ever so grateful for their curiosity, commitment, and creativity. We can’t wait to see how they use their new skills to keep making a difference in the world of data for social good.
Glossary for technical terms
- Python → a programming language used to automate tasks and analyse data.
- Power BI → a software tool for creating dashboards and reports.
- DAX / M code → special languages inside Power BI used to handle data.
- Correlation → a measure of how two things move together (e.g., ice cream sales and hot weather).
- Regression analysis → a method to understand how one outcome is affected by multiple factors.