The goal of this interview series is to inspire and help people to transition their career into a new or next experimentation related role. In this edition Frank Hopkins shares his journey. Frank is Experimentation Lead at BBC and you might also know him from his blog on Medium.
I actively experiment with new tools, technologies, and methodologies in the field of experimentation and data science. Whether it’s testing out a new statistical analysis tool or exploring a cutting-edge machine learning algorithm, hands-on experimentation helps me stay ahead of the curve and adapt to changes in the industry. Building something helps me learn.
Frank Hopkins
Hi there, I’m Frank Hopkins. I’ve been immersed in the world of experimentation, data science, and optimisation for quite some time now. Currently, I hold the role of Experimentation Lead at BBC Studios, where my focus is on using data to make smart decisions and drive growth. My academic background is in biomechanics, which is a branch of sports science that integrates physics and human movement.
In a nutshell, my current job involves designing efficient experimentation pipelines, creating roadmaps for testing initiatives, and collaborating with teams to ensure smooth operations.
Outside of work, I also share insights and tips as a Data Science and Experimentation Blogger on Medium. I’m passionate about sharing knowledge and fostering discussions within the data community.
Excited to share my experiences and chat with you all about the fascinating realm of experimentation and data science!
What is your current experimentation role and what do you do?
Currently, I serve as an Experimentation Lead at BBC Studios. In this role, my primary focus is on leveraging data-driven insights to inform decision-making and facilitate growth across the organisation.
Day-to-day, I’m responsible for designing and implementing streamlined experimentation pipelines, developing comprehensive roadmaps for testing initiatives, and collaborating closely with cross-functional teams to ensure the seamless execution of experimentation processes.
Additionally, I oversee stakeholder management at a company-wide level, presenting statistical insights and findings in a clear and understandable manner to foster informed decision-making.
Essentially, my role revolves around driving experimentation efforts, optimising processes, and ultimately, contributing to the overall success and growth of BBC Studios through data-driven experimentation.
How did you enter the experimentation space? What was your first experimentation related role?
My journey into the experimentation space began with a combination of academic curiosity and practical application. I initially pursued a degree in a related field, where I gained foundational knowledge in statistics, data analysis, and experimental design.
My first experimentation-related role was as an Experimentation Analyst at the BBC. In this role, I had the opportunity to apply my academic knowledge to real-world scenarios, democratising the utility of A/B and multivariate testing across various broadcasting products. This involved consulting with product managers to advocate for the implementation of digital experimentation and harnessing insights from experimentation vendors to provide actionable results.
How did you start to learn experimentation?
My journey into learning experimentation was a blend of formal education, self-study, and practical experience. It began during my academic pursuits, where I delved into courses covering statistics, experimental design, and data analysis. These foundational principles provided me with a solid understanding of the theoretical underpinnings of experimentation. I am largely self-taught in the more technical aspects of experimentation and taught myself to code basic Python and R when I first got into industry.
How do you apply experimentation in your personal life? (what are you tinkering with or always optimizing?)
In addition to my professional endeavors, I’m also deeply passionate about electronic music production and am a semi-professional musician, which serves as another avenue for constant experimentation and creative exploration. As an electronic musician, I’m constantly pushing the boundaries of sound, rhythm, and composition, using a variety of tools and techniques to create immersive sonic experiences.
Experimentation lies at the heart of electronic music production. From exploring new synthesizer patches and sound design techniques to experimenting with unconventional rhythms and song structures, every aspect of the music-making process is an opportunity to push the boundaries and discover new sonic territories.
What are you currently doing to keep up with the ever-changing industry?
1. Continuous Learning: I regularly engage in online courses, workshops, and seminars to deepen my knowledge and acquire new skills relevant to experimentation and data science. Platforms like Coursera, Udemy, and DataCamp offer a wealth of courses covering a wide range of topics, from advanced statistical analysis to cutting-edge machine learning techniques.
2. Reading and Research: I make it a point to stay updated with the latest research papers, articles, and books in the field of experimentation and data science. I regularly read academic journals, industry publications, and blog posts to stay informed about new methodologies, best practices, and case studies. Hopefully, these feed into my blogs. I find writing helps me learn.
3. Experimentation Projects: I regularly undertake experimentation projects, both professionally and personally, to apply theoretical knowledge in real-world scenarios and gain practical experience. These projects serve as learning opportunities and help me stay sharp and up-to-date with industry trends and best practices.
4. Experimentation with New Tools and Technologies: I actively experiment with new tools, technologies, and methodologies in the field of experimentation and data science. Whether it’s testing out a new statistical analysis tool or exploring a cutting-edge machine learning algorithm, hands-on experimentation helps me stay ahead of the curve and adapt to changes in the industry. Building something helps me learn.
What recommendations would you give to someone who is looking to join the experimentation industry and get their first full-time position?
1. Gain Relevant Skills: Develop a strong foundation in skills relevant to experimentation, such as statistics, data analysis, experimental design, and programming languages like Python or R. Consider taking online courses, attending workshops, or pursuing certifications to build your skills and knowledge in these areas.
2. Hands-on Experience: Seek out opportunities to gain hands-on experience with experimentation projects. This could involve internships, freelance work, or personal projects where you can apply your skills in a real-world setting and build a portfolio of work to showcase to potential employers.
3. Networking: Network with professionals already working in the experimentation industry to learn about their experiences, gather insights, and explore potential job opportunities. Attend industry events, join online communities, and reach out to professionals for informational interviews to expand your network and gain valuable connections.
Which developments in experimentation excite you? How do you see the field changing in the next 5 to 10 years?
1. Advancements in Machine Learning and AI: As machine learning and artificial intelligence continue to advance, I foresee experimentation methods becoming more sophisticated and automated. Machine learning algorithms could play a significant role in optimising experimentation processes, from identifying meaningful patterns in data to dynamically adjusting experiments in real-time.
2. Personalisation and Contextualisation: With the growing emphasis on personalised user experiences, I anticipate experimentation strategies evolving to incorporate more nuanced approaches to segmentation, targeting, and personalization. Experimentation frameworks may become more adept at contextualizing experiments based on user behavior, preferences, and intent.
3. Experimentation at Scale: As organisations increasingly rely on experimentation to drive decision-making across various domains, there will likely be a greater emphasis on experimentation at scale. This includes developing robust experimentation platforms, methodologies, and frameworks that can support large-scale experimentation initiatives across diverse teams and domains.
4. Integration of Qualitative and Quantitative Data: The integration of qualitative and quantitative data sources is poised to become more prevalent in experimentation. By combining insights from user feedback, surveys, and qualitative research with quantitative experimentation data, organizations can gain a more comprehensive understanding of user behavior and preferences.
Is there anything people reading this can help you with? Or any parting words?
Thank you for asking! As someone deeply passionate about experimentation, data science, and optimisation (and to be honest, all things science), I’m always eager to connect with like-minded individuals, share insights, and collaborate on interesting projects. If you have any questions, ideas, or opportunities for collaboration in these areas, I would love to hear from you.
In parting, I’d like to encourage everyone to embrace a mindset of curiosity, experimentation, and continuous learning. The world of experimentation is rich with opportunities for discovery, innovation, and growth, and by approaching challenges with an open mind and a willingness to explore new ideas, we can unlock endless possibilities for positive change and advancement.
Thank you for taking the time to read this interview, and I look forward to connecting with you all in the future. Remember, the journey of experimentation is as much about the process as it is about the outcomes, so let’s keep exploring, experimenting, and pushing the boundaries of what’s possible together.
Which other experimenters would you love to read an interview by?
Isaac Newton
Thank you Frank for sharing your journey and insights with the community.
