I work as both a Data Scientist and a Data Engineer.
The essence of my job is data analysis and AI, though it encompasses a wide range of tasks and typically falls into two distinct roles.
However, due to my career experience, my profile sits somewhere in the middle. As a result, I jump in wherever the need arises.
The role of a Data Engineer primarily involves extracting and transforming data through vast automated pipelines, ensuring it’s ready for analysis.
On the other hand, the Data Scientist’s role is to use this data for analysis or Machine Learning models and then present it to the business, often reaping most of the recognition.
The backend involves a significant amount of programming and tech, while the customer-facing aspect focuses on understanding business needs, testing ML models, and visualising data.
This means my setup handles a wide variety of tasks that demand substantial computational power, some of which resides in the “cloud.”
I currently work as a freelancer because achieving a work-life balance has become crucial to me, especially after having children.
In my free time, I write about consumer tech on my blog (I hope you’ll drop by!).
It’s fuelled entirely by my passion and black coffee (I don’t earn any income from it).
I aim to review, recommend, inspire, and simplify complex topics in a grounded manner.
This approach mirrors my professional life, especially in an era where AI is often hyped far beyond its practical significance.
While I’m captivated by technology, I appreciate it not merely for its own sake but for its utility in my daily life or the tangible value it offers.
Many people seem to lose sight of this, often getting caught up in the latest trends and impulsively saying, “I want one of those.”
Outside of work, I enjoy gaming with my oldest child and have a penchant for reading, particularly psychology and fantasy.
Occasionally, I also make an effort to soak up some sun.
