Data Scientist Spotlight: Pearl Mensah

1. Please introduce yourself, what you do, and your current role.

I’m Pearl Mensah, a data scientist specialising in natural language processing and e-commerce analytics. I enjoy utilising data-driven insights to address intricate challenges and facilitate informed decision-making processes. I currently work as a freelance analyst for Decorzone Limited. In the past, I’ve had the pleasure of working with companies like Société Generale and Fairwayrock Components Limited.

2. Tell us about your path into data science. What inspired you to pursue a career in the field? Is this what you’ve always wanted to do?

My childhood dream was to become a lawyer, a path that seemed a natural fit considering my background in the humanities. I followed this path by studying general arts in high school and later focusing on political science and classical studies at university. However, after graduating, I took a different turn and joined a prominent bank.

It was during my time at the bank that I stumbled into the world of data science. Initially, my role involved gathering insights and compiling industry reports. But as I worked with data, I found myself drawn to its incredible potential. I was fascinated by how it could reveal patterns and insights across different fields. This led me to dive deeper into market analytics through hands-on projects, where I discovered my knack for analysis. With encouragement from an in-house data scientist, I decided to pursue further education in data science. It was a leap of faith, but one that I embraced wholeheartedly.

3. What are some of the biggest challenges you have faced on your career journey, and how have you overcome them?

My career journey, like any, has been filled with challenges that shaped my growth. A pivotal obstacle was transitioning from academia to professional data science. While my academic background provided a solid foundation, adapting to real-world applications demanded a distinct skill set. Training in data science often placed me among peers with more technical expertise, which initially sparked feelings of inadequacy. To overcome this, I immersed myself in industry projects and continuously upgraded my skills.

Another ongoing challenge in data science is keeping up with rapidly evolving technologies and methodologies. To address this, I prioritise continuous learning by staying updated with research, attending conferences, and engaging in meetups.

4. What advice would you give to those who are just starting in AI and data science? What skills or qualities do you think are most important for success in this field? 

For those embarking on a journey in AI and data science, my advice would be to cultivate a strong foundation in mathematics, statistics, and programming—the core pillars of this field. Cultivate curiosity to ask the right questions, explore data creatively, and derive actionable insights. Domain expertise is key; understanding industry nuances enhances your ability to drive impactful outcomes. Embrace failure as a chance to learn and iterate. With dedication and perseverance, you can carve out a successful career in AI and data science.