Rebecca Vickery is an award-winning data science leader with over 16 years’ experience in the field of data across diverse industries. A prolific writer, she’s published over 100 articles on Medium, primarily dedicated to guiding others on their journey into data science. Rebecca is also a sought-after speaker at numerous data conferences globally.
Currently, she leads a data team at EDF, developing machine learning models and conducting research to provide insights for targeted customer engagement.
In this post, Rebecca looks back on her journey from an undergrad placement at Kew Gardens to her current role of Senior Leader of EDF UK’s Customer Insight and Targeting team. What were the pivotal moments of her career, and what lies in store for the future?
My career in data started a long time before cloud computing had become established. Maintaining records of plant genome size at the Royal Botanic Gardens Kew during my undergraduate placement in an on-premise Microsoft Access database, was very different from the work I’m currently doing running machine learning workloads on Amazon Web Services (AWS) and Snowflake. However, it sparked a passion for data discovery, science and problem-solving that ultimately would lead to a career in data science.
Inspired by a birthday gift of a microscope at age ten, I developed a keen interest in science and nature from a young age. As a result, I chose to study Molecular and Cellular Biology at university. An industrial placement year at the Royal Botanic Gardens Kew involved early work with databases and writing basic code. After graduating from university in 2001, keen to further pursue programming, I took a trainee role in a media agency to learn front-end web development.
Data analysis gradually became another part of my role at the agency as Google Analytics became established and clients had a desire to understand how their websites were performing. Finding a particular passion for analytics I applied for a role with an online travel retailer, Holiday Extras, where they were looking for someone to build a new web analytics practice.
I would spend the next few years working to establish a suite of web analytics reporting tools using an in-house event tracking system. To begin with, my work mainly centred around SQL and Excel, but as the business rapidly underwent a digital transformation so did my role. The company’s data migrated to Google Cloud, and as an early adopter of the business intelligence tool Looker, I was able to automate much of the manual data analysis that previously consumed my time.
I now had an increased capacity to perform more strategic data analysis and build models. Quickly finding the limits of SQL and Excel, and keen to expand the impact of my work, I started on a journey to learn Python for Data Science. At the time I worked a full-time job and had two young children, so studying had to fit into the limited free time that I had. I was fortunate to have an experienced data scientist as a mentor who guided me in designing a bespoke curriculum to learn from. Data science is a cross-functional field and the chances are that anyone starting out will already have some of the skills required. I already had a background in data analysis and statistics so for me, the main focus for learning was programming in Python and machine learning.
Focussed on learning as much as I could in limited time, I quickly developed a technique for accelerated learning. I would use a variety of resources, mostly freely available tutorials and articles on the internet. I’d learn just enough to be able to apply these skills in my work and then explain the concept back by writing a blog post. This act of explaining would guide me to develop a deeper understanding of the topic and would solidify my knowledge, but most importantly it would tell me where the remaining gaps were in my understanding. I would then repeat the process, learn more, and build even better applications.
Writing has since become another passion of mine and continues to be a source for learning and development as well as enabling me to give back to the data science community who helped me so much at the beginning of my career. I wrote my first article on Medium in 2018 and to date, I have published over 100 articles on the platform.
Being driven by the impact that my work in data science could have, I later applied for a data scientist role at EDF UK. EDF’s core mission is to help Britain achieve Net Zero and I was hugely inspired by the opportunity to apply my skills to solving a problem that affects everyone and the world around us.
Two years later, I had the chance to step into a leadership position by taking on a role as Senior Leader of a brand new Customer Insight and Targeting team. My team and I are responsible for building models, research insight and data sets to develop audience segmentation and improved targeting across digital, media and direct marketing. I remain partially hands-on and now also have the opportunity to train and mentor others in Python for Data Science. Moving into leadership has allowed me to grow my impact even further through guiding and directing an entire team.
I’m not sure there is a traditional route into data science and my journey certainly wasn’t a direct path. My focus has consistently been on the impact of my work, continuous learning, and the joy derived from the process, rather than adhering strictly to job titles, and this has resulted in an interesting and varied career in data.
The data space has never been more interesting than it is right now. The explosion in generative AI this year has made machine learning more mainstream and it seems that everyone is talking about it now. Unlocking the potential of machine learning used to be a significant challenge for many organisations, but I think we are now at a tipping point for driving substantial positive change. In my new role, I am already witnessing the tangible effects of this shift and I’m excited about the future of data and the impact it will have on the world as we know it.
My views expressed in this article are entirely my own and don’t necessarily reflect the views of any of the companies mentioned.
