Data Science is often described as the intersection of statistics, information theory, and computer science, but to Ayesha, it’s simply about seeing the world through a clearer lens. 🔍
She recently spoke to the Naimuri team to reflect on her journey from studying Physics at Warwick to completing her PhD in Mathematics at Bristol, and how that theoretical foundation fuels her work as a Data Scientist at Naimuri.
In this Q&A, she shares:
- The Superpower of Adaptability: Why staying ahead means being a perpetual student.
- Collaboration over isolation: Breaking the myth of the lone scientist in the lab.
- The Reality of the role: From high-level AI assurance to the nitty-gritty of data wrangling.
If she could tell her 15-year-old self one thing, it would be this: your career path doesn't have to be linear for it to make sense. Follow your curiosity, it all connects in the end.
Read the full interview below ⬇️
Name and Role
Ayesha Irfan, Data Scientist
How would you describe your role at Naimuri to someone who has never heard of Data Science before?
Data Science is essentially about seeing the world through data. My role involves analysing vast, often complex datasets to recognise patterns, which allows us to make powerful predictions or highly informed decisions across various industries.
It’s a deeply interdisciplinary field that sits at the intersection of statistics, information theory, and computer science.
For example, in the medical industry, machine learning models are already transforming lives by uncovering patterns human analysis might miss.
At its heart, Data Science is the engine that improves our world, one dataset at a time.
Walk us through a typical day. What are the specific 'puzzles' or challenges you are usually trying to solve?
The wonderful thing about being a data scientist is that each day brings a unique set of puzzles. We are typically given huge collections of raw data and tasked with the challenge of making sense of it.
The primary focus is often data wrangling and refinement: before a dataset can be used to train a machine learning model, we must clean it eliminating outliers, managing missing values, and structuring it efficiently.
This requires significant creativity, as we decide how to organise the data, identify the most useful signals, and write code to filter and elevate the quality of the information.
Beyond the technical work, we invest time in contextual research, exploring the real-world implications of the data and our model outputs due to the diverse applications of the field. I also really enjoy engaging with the theoretical side, such as the in-depth mathematics involved in AI assurance.
What is one soft skill (like empathy, creativity, or communication) that you use more than people would expect in a technical role?
Adaptability. The technology landscape evolves at a rapid pace. In every new project, you will inevitably be surrounded by new tools, frameworks, and techniques. Maintaining an open mind and a continuous desire to learn is the most critical superpower for staying ahead in a technical role.
Did you always have a 'science spark' growing up, or was it a discovery you made later in life?
My interest in science was a gradual evolution that developed over time, rather than a singular 'spark' or moment of discovery.
Was there a specific teacher, mentor, or moment in school that shifted your perspective toward science?
I had an inspiring secondary school science teacher who was a biochemist. She fostered a true passion for learning by teaching us about anything we became curious about, regardless of the official curriculum.
She often showed us how concepts in Physics and Chemistry deepened our understanding of each other.
She made me excited about pursuing science and, most importantly, exposed me to the highly interdisciplinary nature that defines most scientific fields.
What did you study at University, and how did those studies prepare you for the real-world data challenges you face now?
I studied Physics at the University of Warwick, an exceptional course that provided a strong foundation for a career in Data Science. Physics employs the toolkit of mathematics to unravel complex real-world systems, which is essentially the core task of a data scientist.
Crucially, the scientific computing modules gave me early exposure to programming languages like C++ and Python.
By the time I joined Naimuri as a graduate data scientist, I was familiar with Jupyter notebooks, building machine learning models, and application deployment, many of these skills were honed during my undergraduate studies.
I am currently finishing my PhD in Mathematics at the University of Bristol.
Gaining a deeper, theoretical appreciation for the ideas underpinning Data Science has significantly improved my capabilities.
A data scientist constantly encounters complex mathematical formulae and diagrams explaining model architectures or measuring performance, and fluency in that language is a huge advantage.
Did you ever feel like you didn't fit the mold of a traditional scientist? If so, how did you overcome that?
For most of my life, my impression of a scientist was that of an independent, lone worker who disappears into a lab and returns with a discovery.
As someone who thrives on collaboration and generating ideas in a group, I worried I wouldn't suit the reality of a scientific career, even though I loved science itself.
However, I now see that modern science is far more collaborative than I ever expected.
Progress is faster and more reliable when we collectively ask questions, design stronger experiments, and interpret results as a team.
Bringing diverse perspectives to a problem leads to the most meaningful outcomes.
I actively seek and encourage team-based work, pair programming, and brain-storming sessions, creating a comfortable environment where everyone feels safe to ask questions. This ensures we all learn faster and produce higher quality work.
For those considering a Science background, do you think the career opportunities are as broad as people say?
Science-based career opportunities are not just broad; they are virtually limitless, offering a foundation that can pivot into almost any industry imaginable.
What is the biggest misconception about being a woman in Data Science that you’d love to set straight?
There is a stubborn misconception that women in data science are either better suited for or only interested in soft Data Science work, as opposed to harder domains like complex machine learning or data engineering.
This idea often stems from the simple fact that there are fewer women currently pursuing those specific domains. This is deeply unfair. Given the existing underrepresentation of women across the entire tech industry, it is a reflection of accessibility and historical barriers, not a lack of interest, ability, or ambition among women.
Name three top ways ways to inspire more girls to study Science?
- Direct mentorship programs with professionals
- Re-branding Science to emphasise creativity and social impact
- More visible female role models in media
What needs to change in the wider world (beyond just school) to encourage and retain more female talent in tech? (e.g., workplace policies, industry perception, etc.)
We need to recognise the profound power of representation.
It can take meeting just one professional pursuing a career a child has never seen before to change the entire trajectory of that child’s life.
Young people’s interests must be encouraged, and more companies should actively participate in careers events specifically designed to inspire young women and girls.
Letter to Your Younger Self: If you could go back to your 15/16-year-old self when she was picking her subjects, what would you tell her?
Pursue everything you find interesting; do not worry about your career path needing to make sense in a linear way, because in some way, everything you learn will connect. Remain deeply curious and learn about anything that genuinely intrigues you, it will help you in unexpected ways later down the road!
