Get to know: Ronan Bradley, VP of Product Analytics and User Research for Facebook at Meta
Q: Let’s start with the basics… What is your role at Meta?
Ronan: I lead Product Analytics, including Data Science and Data Engineering, and User Research for Facebook at Meta. I also lead Product Analytics across the Family of Apps, working closely with the other product analytics leads for Instagram, WhatsApp, Messenger, GenAI and Privacy to identify and answer key questions about our business and to ensure we’re building and retaining the best Data Science and Data Engineering talent.
At Meta, we frame our product work into three categories: Understand (the people problems and the value of our apps), identify (how to best address those people problems or business risk) and execute (implementing the solutions we identify). In my role, I work closely with the insights teams to understand user needs and behavior and how our business is trending; to identify strategic opportunities in partnership with product leadership; and, in partnership with our engineering teams, to track how we execute our roadmaps and whether we have met user needs.
Q: How did you end up at Meta?
Ronan: I joined Meta over 11 years ago as a senior individual contributor and then manager, originally working on games payments in Meta’s Dublin, Ireland office. At the time, gaming on Facebook was a large part of our business and one of the areas where we first applied product analytics as we know it today. From there, I moved to the Integrity team, where we were focused on understanding how to build and monitor integrity systems. Then, nine years ago, I moved into the Facebook Data Science team and began working on the core product.
When I joined Meta, the company had already made a name for itself as a place to work on “big data” and with cutting-edge data technology. It was already recognized as a company that used data to inform decisions and create a better user experience. More than that, it was and remains a company where Data Scientists are an integral part of every stage of the product life cycle and core to decision making.
During my long tenure, one of my favorite things about working at Meta has been the opportunity to work in a variety of areas. Even today, the type of work that Data Scientists and Data Engineers do varies depending on the nature and maturity of the product they are working on. For example, you could be working on a new product as the first data scientist or Data Engineer helping to bring early understanding to user behavior of the product, or you could be working on very mature products where you’re applying cutting-edge Data Science techniques to continue to iterate on a product that is already well understood.
Q: What is the best career decision that you’ve ever made?
Ronan: During my career, many of the best decisions I have made have also been the hardest ones. I have made moves across companies and functions–from engineering, to product management, to leading an enterprise software startup, to Data Science at Meta. With each of those moves, there was a great deal of uncertainty and risk. However, each move also got me out of my comfort zone and required me to rapidly learn new skills and contexts, which, for me, is the most important thing. So in the end, even though not every risk paid off entirely, the risk taking was worth it.
Joining Meta has been the best decision of my career to date, as it has provided me with the ability to continue to learn and grow as a leader and a Data Scientist. I am inspired knowing that the work of my team informs the experience of the more than 3 billion people who use Facebook each month.
Q: What advice do you have for Data Scientists or Data Engineers who are considering moving to another function?
Ronan: My advice would be the same to somebody who was a product manager or engineer as well. I have found that as I have grown in my career, many of these roles begin to overlap (particularly at Meta) between being a data leader, a product leader or an engineering leader and an organizational leader. While senior leaders will spend their time and focus more on their functional role, they also wear product and org leader hats.
Earlier in your career, functional distinctions are perhaps felt more acutely but it is important to recognize that they are not one-way doors. Ultimately, each career decision should be more about what will motivate you to acquire new skills and the experiences, which will make you successful over the long run. If you are still learning and developing your skills, there is no rush to move to a new function or a new company or whatever. At Meta, in particular, because of the scale of our Data Science and Data Engineering teams, it is easy to find a new project or role that allows the individual to develop new skills and even, on occasion, transition to other functions.
Q: What is something that analytics professionals typically learn too late in their careers?
Ronan: Initially, Data Scientists and Data Engineers are often focused on honing their technical skills over the broader set of communication skills. Technical skills are very important because they will allow you to have impact and move quickly and, as you become more senior, they will allow you to understand what is possible, even if you do not write code yourself. However, it is equally important to be able to deeply understand the business question or need, translate it into the correct analytics question and then translate the results back into the business context without losing the precision and rigor of the technical work. One trap I see even technically expert Data Scientists falling into is under-investing in these broader skills and, therefore, not answering the actual business questions or not being able to explain the answer to a non-analytics audience.
Q: How do you see people, teams, and organizations holding themselves back?
Ronan: There are two patterns that I see consistently occur. One is confusing domain knowledge with skill sets. For example, early in my career, I worked in payments and risk, without any prior domain knowledge. Many of my colleagues believed that risk was their expertise and were uncomfortable in other domains because they conflated domain expertise with their core ability. Instead, applying experiences and skills to new domains is important and often unlocks new ideas that have not been previously considered. It also provides confidence to realize that your core skills (like rigor, technical skills, flexibility, and creativity) are not domain specific and you can apply them to a much broader set of problems.
Another involves the tendency to view one’s career in a linear manner. This perspective can lead to the avoidance of opportunities that don’t seem to directly advance one’s career progression as measured by levels and titles. Essentially, people might miss out on valuable experiences because these opportunities do not obviously contribute to their career advancement. Eight years ago, I took a new role and transitioned from leading a team of seventy to a team of five. Some people might interpret this as a big career stepback but ultimately, this change allowed me to get closer to the Data Science work and really re-energized my commitment to the function and the company. It also gave me confidence in my ability to deliver value, influence product strategy, build teams, all of which set me up to take on my current role a year later.
Q: As you reflect on your career, what are the differences you see across each stage of your career progression?
Ronan: I am going to answer this across all stages of my career rather than stage by stage. I remember when I went from being a “doer” to a manager and was worried that I was no longer practicing my craft and felt that I needed to stop ‘being technical’. In many companies and for many individuals, this is real and there is a dividing line between the individual contributor and manager. One of my favorite things about Meta is that at every level of people management within Product Analytics, you are both leading and also practicing analytics. For me, the real difference is that the more senior you become, the more you are required to context shift more quickly and move easily from thinking strategically to thinking tactically.
Q: What behaviors and skills have led to your success as a data scientist and manager?
Ronan: I will give two examples: The first applies to both individual contributors and managers; the second is particularly important to managers of any insight function including product analytics and user research.
The first example is: Given the range of problems we face as Data Scientists and Data Engineers, we must have the flexibility to apply our skills to new types of problems. Throughout my career, I’ve built a diverse toolkit and playbook. This approach helps me to quickly recognize the nature of a new problem. Once I understand the problem, I can then choose the most suitable tool or method to tackle it. In this way, I applied so much of what I learned in my first role on Gaming to Integrity and then to all aspects of Facebook.
This concept applies to management, as well. At Meta, as an analytics leader, I’m not only overseeing others, I’m also doing actual analytics work. In my day-to-day, I use the same pattern recognition to solve problems and translate business questions to the technical domain as I do in solving problems related to my teams.
The second example is: To be a strong leader of any organization, you have to think about the core needs of the people on that team. One of the most important things we can do as insight team leaders is to give our teams the knowledge that they are supported and empower them to feel comfortable identifying and escalating the truth as they see it reflected in the data. To do this, we must create an environment with a high degree of integrity and explicitly defined principles. This enables the team to have clarity in why the work is being done, what is being asked and, ultimately, why we should feel empowered and proud to deliver the results even if they contain uncomfortable truths.
Q: What is your favorite thing about working at Meta?
Ronan: I have a few answers to this question. First, I love that as a people manager and leader, I can also practice Data Science and am continually learning. When people think about analytics , they often think about writing code or conducting analyses, both of which are aspects of our work. Equally valuable, though, is the ability to establish a framework for complex and ambiguous problems to enable the analysis and provide clarity to the product problem. As an analytics leader, while I am not coding, I regularly think about and work with the teams to translate big, abstract problems or questions into more concrete and solvable ones.
I also value the scale and complexity of our business. There are constantly new problems that require Data science to solve. When I came to Meta, I thought I was joining a company that had already “figured it all out” and that I would learn and move on to a new company within a few years. More than 11 years later, I’ve come to appreciate that we have really just begun. While we have so much more understanding and data than when I started, there is so much more we can learn to deliver more value from the app for users.
