Nour shares how her exposure to data-driven projects through degree programmes at EEE ignited her passion for the field and equipped her with the technical and problem-solving skills essential for her roles at Arup and Just Group. Drawing on her UCL education, she not only applies advanced machine learning techniques in her work but also gives back by mentoring others, inspiring the next generation of engineers and data scientists to follow in her footsteps.
Can you share what initially sparked your interest in data science and how your degree in Electrical and Electronics Engineering at UCL shaped your career path?
My interest in data science started during my time studying Electrical and Electronics Engineering at UCL. While the degree offered a broad foundation, I was particularly drawn to the digital aspects of the curriculum. This exposure helped me realise my passion for working with data and the transformative impact it can have across industries.
The field of data science caught my attention because of its rapid growth and the endless opportunities it presents. I enjoy the challenge and creativity involved in analysing data to uncover insights, solve problems, and drive decisions. Besides, the flexibility that data science offers in terms of work environment and diverse applications aligns perfectly with what I envision for my career.
These elements collectively shaped my decision to pivot towards data science and have since fuelled my dedication to developing expertise in this dynamic and exciting field.
After graduation, you joined Arup as a Graduate Data Scientist. What were the biggest challenges you faced in transitioning from academia to industry, and how did you overcome them?
One challenge was trusting myself and my abilities in a professional setting. In academia, you're often focused on individual work or theoretical concepts, so stepping into an environment where you’re expected to deliver real-world impact felt like a big leap.
Another challenge was navigating meetings with senior professionals. Initially, it was intimidating to share my thoughts or present my work to people with so much experience. On top of that, the consulting nature of the role meant I had to learn how to switch between multiple projects running in parallel, which was a big shift from the more focused approach I was used to in academia.
What really helped me was the supportive environment at Arup. I also had the advantage of completing a three-month internship with the company before starting full-time, which gave me a good sense of what to expect and helped ease the transition. Those experiences, combined with the encouragement from my team, made the process much smoother and helped me grow more confident over time.
Over your years at Arup and Just Group, what skills or experiences have been most instrumental in your career progression as a data scientist?
Over the years at Arup and Just Group, several factors have been instrumental in my career progression as a data scientist. One of the key aspects has been the diverse range of projects I’ve had the opportunity to work on. Each project comes with its own unique challenges and requirements, which has helped me build a broad skill set and adapt to different domains and problem-solving scenarios.
Another significant factor is the ongoing advancements in the field of data science. It’s an area that’s constantly evolving, and staying up to date has been both a challenge and a rewarding experience. Continuous learning, whether through new tools, techniques, or methodologies, has played a big role in shaping my expertise.
As the years have gone by, I’ve taken on greater responsibilities, from leading parts of projects to mentoring others on the team. These experiences have not only strengthened my technical abilities but also honed my communication and leadership skills, which are just as important in driving a successful career in data science.
With expertise in tools like Python, SQL, and machine learning technologies, what advice would you give students looking to build technical proficiency in these areas?
My advice to students is simple: NEVER STOP PRACTICING. These skills require consistent hands-on experience, and the best way to learn is by doing. Whether it’s through personal projects, internships, or coursework, applying what you learn to real-world problems is essential.
It’s also important to stay up to date with the latest advancements in the field. Technologies and best practices in data science evolve rapidly, so being proactive in learning new techniques and tools will give you an edge.
During my time at Arup, I took the initiative to go back to UCL and complete three additional machine learning modules. That experience not only helped solidify my understanding of key concepts but also gave me the confidence to apply them effectively in my work. Always be open to learning and find ways to build on your foundation, it’ll make a huge difference as you progress in your career.
Could you highlight a particularly exciting or impactful project you worked on whilst at UCL that helped you later on in your career?
One particularly exciting project I worked on at UCL was my third-year project with Dr Ryan Grammenos, titled "Real-Time Data Analysis for IoT." This was my very first data science project, and it played a pivotal role in shaping my career. The project involved analysing and processing real-time data from IoT devices, which introduced me to the core principles of data science, including data handling, analysis, and deriving actionable insights.
It was during this project that I realised how much I enjoyed working with data and the potential it holds to solve real-world problems. This experience motivated me to pursue a data science internship at Arup, which became a stepping stone to building my career in the field. Looking back, that project was not just a learning opportunity but also the spark that set my career in motion.
Your experience as a STEM Ambassador and a primary school tutor demonstrates a commitment to giving back. How have these experiences influenced your professional or personal outlook?
My time as a STEM Ambassador and primary school tutor has greatly influenced me. It’s been rewarding to inspire curiosity in young learners, and it’s strengthened my ability to simplify complex ideas which is an essential skill in data science when communicating with non-technical stakeholders. These experiences also deepened my appreciation for mentorship and giving back, which continue to shape how I approach my career and community.
While working, you completed MSc Machine Learning CPD Modules. How did you manage balancing work with further studies, and would you recommend a similar path to others?
I would definitely recommend balancing work with further studies, like the MSc Machine Learning CPD modules I completed. The coursework was a great technical exercise, and the theory behind it was fascinating to understand. I managed it by attending lectures on Tuesday evenings and dedicating my evenings to coursework and studying. While it was challenging at times, the key benefit is that much of what you learn is directly applicable to your work, making the effort well worth it. The combination of practical and theoretical learning truly enhanced my skills and my confidence in the field.
As a female data scientist with a history of mentoring others, what steps do you think the industry can take to encourage more diversity in STEM fields?
I believe the industry can take significant steps to promote greater diversity in STEM. Creating supportive environments where diverse voices are genuinely heard and valued is essential. Mentorship programmes, like those I’ve participated in, play a pivotal role in empowering underrepresented groups by building their confidence and providing guidance throughout their careers.
In addition, increasing the visibility of diverse role models and celebrating their success stories can also inspire others to pursue opportunities in STEM. Inclusive recruitment practices are crucial to ensuring equal access to growth and progression for individuals from all backgrounds.
I also believe that encouraging collaboration and valuing diverse perspectives within teams can help break down barriers and make STEM fields more welcoming and accessible to everyone. These steps are key to driving meaningful and lasting change.
What drives your passion for data science and technology, and how do you stay motivated to tackle the challenges in your field?
My passion is driven by how rapidly the field is evolving. There’s always something new to learn, whether it’s a cutting-edge tool, technique, or technology, and that constant evolution keeps things exciting.
I also love the fact that data is everywhere, it’s fascinating to work with datasets I never imagined I’d have access to. Each project feels like a new puzzle, and the process of uncovering insights and creating solutions is incredibly rewarding. This variety and the opportunity to continuously grow and experiment keep me motivated to tackle challenges when they arise.
Looking back on your journey, what key advice would you offer to prospective or current students aspiring to follow a similar path in technology and data science?
Never feel discouraged about learning something new, even if it wasn’t the main focus of your course or degree. Curiosity and interest are all you need to start exploring. Some of the most rewarding opportunities in my journey came from following my curiosity and stepping outside of my comfort zone. Embrace continuous learning, it's a mindset that will serve you well in such a dynamic and evolving field.
