Stepping outside of our functions as statisticians with your career

Transcript:

Alexander: You’re listening to The Effective Statistician podcast, a weekly podcast with me, Alexander Schacht and Benjamin Piske, designed to help you reach your potential, lead great sciences and serve patients without becoming overwhelmed by work. Today, we are talking about stepping outside of our core functions as statisticians with your career and what you can learn from that. So stay tuned for this episode with Liz. 

We had a couple of different episodes of powered people stepping outside of their career and if you want to look into these others, just scroll through the other episodes on your podcast player and you’ll surely find a couple of others that will be of interest for you. 

This podcast is produced in association with PSI, a community dedicated to leading and promoting the use of statistics within the healthcare industry for the benefit of patients. Join PSI today to further develop your statistical capabilities, with access to the ever-growing video-on-demand content library, free registration to all the PSI webinars, and much, much more. Head over to psiweb.org to learn about it and become a PSI member today. 

Welcome to another episode of The Effective Statistician and today I’m speaking with Liz Thompson, how are you doing? 

Liz: I’m great Alexander. Thanks a lot for having me. 

Alexander: Very very good. We got introduced by a common friend Jenny Devenport, who also works at Roche and we work together on the medical affairs called Launch and Lifecycle special interest group. But we are not talking about that part, we are talking today about stepping outside of statistics and maybe you can talk about this. You can introduce a little bit of yourself and what you have done until you step outside of statistics. 

Liz: Yes, sure. Yes I can. So yes, as you said, my name is Liz Thompson and I work at Roche Pharmaceuticals in the UK and Jenny is in Switzerland and I’m in the UK. I’ve been here for about the last 15 years at Roche for quite a long time. I’ve been a statistician in the pharmaceutical industry for over 30 years. So I’ve worked for a number of different pharmaceutical companies, I’ve worked at a CRO, I’ve worked in the UK, I’ve worked in the U.S., in a range of different places, different therapeutic areas and an increasing level of responsibility. As you progress in your career, take on some line management, some leadership and responsibility for part of the portfolio. 

Alexander: Okay. Very good. What did you like so much about being within statistics? 

Liz: I think for me I love being a statistician and I truly believe that statistics and data science in a broader sense can really make a material difference to patients by being their voice to allow patients to bear their data to speak. And I think statisticians have a unique ability to interpret that data and give that full voice to the data. 

Alexander: Yeah. With all the strengths and all the limitations, and it was possible to say with the data by patient level data because very often all our colleagues when they speak about data actually refer to the results, the patient level data.

Liz: Exactly. 

Alexander: Okay, very good. At some point, you step outside of statistics which is I’d say quite unusual thing. I don’t have a statistic about it but my perception is the vast majority of statisticians stayed within statistics for their whole career. What made you consider that step outside of statistics?

Liz: I think you’re absolutely right. Most statisticians do stay in that career for a long time and as I’ve said, I love being a statistician. I love leading statisticians and developing the next generation of statisticians. I’ve always advised the statisticians that I worked with to think about what they are really passionate about. What are they good at? Where can they really add value to society, right? 

Alexander: Where are they good at, what type of strengths are you thinking about?

Liz: I think one of the reasons why statisticians tend to stay where they are is because they focus only on those technical skills they have as a statistician. In a fairly broad sense, the technical skills allow them to influence drug development, their design, their strategy, and things like that but it’s really about those statistical skills that allow them to influence the conversation. What I’m thinking about is, what else are you good at? For me, I’ve got 30 years of drug development experience. 

Alexander: Yes. 

Liz: I’ve been to many health authority interactions. I developed all kinds of different medicines in different disease areas with different kinds of data and study designs. I’ve got a broad range of experience. I’ve also got a good network across the organization. So for me, when I think about it, when I reflect on myself, what did I want to do? Where was my passion? I was thinking that I’m really passionate about developing new molecules into new medicines that can directly impact patients, right? I mean that’s kind of what we’re all here for. 

Alexander: Yeah. 

Liz: But it was actually being part of those conversations as building the strategy and the cut and thrust of drug development, I wanted to be in the room rather than leading an organization that was in the room.

Alexander: Instead of being kind of a functional leader. 

Liz: Why? So I wanted to use that 30 years of drug development experience directly on the molecule programs rather than on that as you said functional leadership. It was about using all of my skills, not just my statistical skills. So I was thinking about that and thinking about where I wanted to develop. I think in the future, Roche organizations go to more streamlined, flutter organizations, there’s going to be less need for many, many layers of functional leaders. We move away from that hierarchical approach, but actually, what we need is to have more leaders directly working on the molecule programs. So I’m thinking about that, thinking about what I’m passionate about. And then we hit 2020 and Covid, there was a call to action. We needed to develop new medicines in a new disease area where nobody knew what the disease really was. Never mind what the endpoint should be, the design should be, how you would get it to regulatory authorities, etc. There’s a call to action for leaders to flow to this work that there was an important need and it just for me was the right thing at the right time that this was really calling out to my broader skill set. And that’s when I took on this role as a global development leader for a program in Covid-19 disease area and I’ve been doing that for about a year now. 

Alexander:  That’s pretty cool. I love how you talk about these collections of skills. These collections of skills in terms of statistical skills, knowing about the design of studies and these kinds of things, and how you analyze studies. And then the second part which are more transferable skills. I know you know about track development overall, you know about interaction space health authorities, the leadership skills that you mentioned, lots of these things. Is there anything in between that you would say these are skills that are also transferable? Let’s say a specific for a statistician or specifically strong at. Because of course, kind of working with regulators, you know, physicians have that regulatory affairs that probably the most and you have known about drug development, well everybody that works for a long time and that probably had that. Are there any skills where you would say, these are transferable skills that statisticians are really good at?

Liz: I think yes you’re right. There are a lot of these transferable skills I have that many other people have as well. I would say the key thing for me is that logical data-driven thinking is something that statisticians don’t even think about as a skill, just like who they are, where there is, it’s in their DNA. And that’s not necessarily so in other parts of the organization there’s much more of a still a scientific base but more of a gut feel and an intrinsic belief in what is the right thing to do. So I think it’s that logic, that great data-driven decision-making, it’s that ability to assimilate a lot of information very quickly and analyze it very quickly. And also, I think it is an ability to evaluate evidence looking at it from all sides. So looking at, not just the data, but the whole ecosystem that you’re gathering that data from. What are the pros? What are the cons? What are the risks you’re taking? Is that a risk worth taking? All of those kinds of things. I think statisticians naturally can think that way. And then the other thing I would say, some of my colleagues would say, they often refer to me and I don’t know whether this is me or statisticians, I think it’s more common among statisticians, it’s with a common sense department. That’s a much more natural evaluation of what we’re trying to do from the logic and the common sense framework.

Alexander: Common sense department. That’s an interesting question. What would be the opposite of that common sense?

Liz: There are a lot of people in our organization who are really, really talented scientists. Maybe they’ve come from academic backgrounds, they don’t have as much drug development experience and they will get very passionate and enthusiastic and get driven by the enthusiasm particularly when they treat patients. I’m holding that in their mind and that’s what’s driving their thought process.

Alexander: Yeah. Is it kind of for these very very patient driven people that are very enthusiastic, is that they are potentially too much afraid of killing a project, for example. 

Liz: Yes. I mean, I think it could be killing the project, it could be maybe moving forward with a project but with a realistic assumption of what the risks are they’re taking because it’s okay to take risks, right? 

Alexander: Of course, yeah. 

Liz: If we don’t take risks in drug development, we won’t get anywhere anytime soon. So it’s being able to go in with your eyes open. 

Alexander: Yeah. That’s a very, very good approach. How about being pragmatic? Is that something that statisticians are very good at? Or is that something where you would see that not a specifically striking component for statisticians? 

Liz: No. I think you’re right. I think most statisticians are pretty pragmatic. I think that’s part of it and I think there’s a willingness to find solutions as well, pragmatic solutions. 

Alexander: Yeah. That’s really, really good. I love that we talk about it because I very often have perceptions that if you think about how you develop your skills, these are the unknown knowledge, so to say. If you develop knowledge you step into, you don’t know what you don’t know, then you know what you know and then you actually don’t know what you know. Most of these things that you just talked about, especially this abstract thinking and that stuff exists, we are far in those last fourth point where we take things for granted. We even don’t take the exam for that but we just don’t think about that because it’s so natural. We have trained for years and years, maybe for decades, to think that way. We assume this is nothing that makes us special but we should recognize this kind of strength and I think these help us to or should help us to be more confident to step outside of statistics and in other roles. 

Liz: Yeah and I could agree more. I think one of the things we shouldn’t underestimate is that broad experience and that way of thinking and what value that adds. I think the other thing that was going to say actually from statisticians is that statisticians can work and often do work across the enterprise from research to marketing and anywhere in between. And that gives you a real breadth of experience about what it takes to make a new medicine that is really helpful. This is one thing that I think we shouldn’t underestimate and it comes into your category, you don’t even realize you have it, is your network. It’s the networks you build over time working across a range of different programs and therapeutic areas and geographies and things like that, really expands your network and your relationships with people. And one thing that I would say is I have a network of relationships across Roche and across the industry as well, through things like PSI where we’ve developed those networks and relationships over the years. I think it is particularly strong amongst the data science and statisticians community. 

Alexander: Yeah. I think I had said this very earlier when I was at University, someone else told me, ‘Well, great leaders have great networks’, because that helps them to solve lots of problems, get feedback, get understanding, get help that support and but it also takes quite a lot of work to do to build these networks. What would you recommend to someone that is let’s say early in their career, let’s take particularly 3 or 4 or 5 years in their career to build these networks? 

Liz: I think, my personal advice would be to get involved in a range of things. So it’s very tempting particularly in that time of their career, to go deep, to be really focused on maybe one particular molecule project in one disease area and you’re trying to build your experience and your credibility and you want to take on a more senior role in them. And to do that, the desire is to go deep, I need to be a real expert, right? And that’s true. That’s an important part of how we grow as statisticians. What I wouldn’t let be that be the exclusive thing that you do, get involved in other things and things in your organization that don’t necessarily just have a statistics label on it. So for instance, later on in my career, I was the Pharma Development Site Head for the UK. 

Alexander: Okay, cool. 

Liz: And so I was helping to lead the UK site alongside the UK commercial company, which gave me a huge insight into what it’s like on that side, it gave me a lot of different networks and people who I could relate to or experts I could tap into, experiences that I never thought that I would ever have. So for instance, this is not the first interview I’ve done. I’m an expert in any way, but I’ve done them before because of that role as the Pharma Development Site Head and you start to make connections outside of statistics or outside of Roche that helped you in different ways. Now how do you make time for that? I think it’s about thinking of yourself as a whole person at random. Part of what you do is that the job, that technical statistician but part of who you are is a leader. And even if you’ve just three years into your career, you’re a leader in waiting and you need to fill that experience out. So it’s really about finding the opportunities that don’t always take a huge amount of time. They just take some conscious effort to do it. 

Alexander: Yeah, I’ve very often recommended it, especially if you sit in these affiliates, even if you have a global role and you sit somewhere in the city and have lunch with people outside your usual colleagues. Have lunch with someone from marketing, someone from PRA, maybe salesforce persons in the affiliate. Just stick with them about what’s on their mind. What are they worried about? What are their goals? Then you can always at the end of the discussion ask, anybody else you should recommend I should speak to. In that way, you can grow your network and also increase your knowledge about your overall industry. And that has proven to be really, really successful in creating these connections, but also opening up opportunities. 

Liz: Yeah, absolutely.  You get something out of it as well. It’s not just about networking for networking’s sake. It’s an enjoyable part of the working life, but you can do it in any number of ways. I know in Roche, there’s a Roche choir that goes across the whole site. You can be from anywhere at any stage of your career, any kind of person, not me, can’t sing a note. But I know, there are other people who have really got to know each other just by being part of the Roche choir. It’s just thinking about where you can do that

Alexander: Yeah, I completely agree. When you step outside of statistics, what were your specific skills that helped you be successful there? 

Liz: So I think we’ve talked a little bit about being aware of the whole enterprise of drug development. We talked a little bit about networks, really important. I would say, particularly for this project, I was in a collaboration with another company. So the skills of being able to work with another party who have aligned different interests to you. I think being able to understand that and build trust is really important, but we were working at breakneck speed, in an emergency situation and being able to evaluate data quickly and knowing it won’t always be complete or perfect but being able to advise the team on what risks they were taking and what was solid that they could make decisions on, that was really important. 

Alexander: Yeah I can imagine. I’ve seen teams debating for weeks and months of what the data means and not being able to make decisions to move forward. I think that is one of the key problems that we have. We just very often provide data but not help the people understand what the insights are from these data. What are really the, you know, not just the table but what kind of assumptions can be made from the table? What kind of conclusions can you make? What kind of actions would be the next steps? All these different things.

Liz:  What do you know about the data? So really being able to do that and also getting people to be able to move on when something isn’t perfect. Not trying to hide that it’s not perfect but being able to say, ‘okay, maybe this data isn’t as complete as you would like it to be’. What are the potential implications? Well if it was complete, it could have said this, it could have said that. But it’s unlikely that it would have been this scenario. 

Alexander: Yeah. 

Liz: So you can be confident if somewhere in between A and B. So I think being able to do that and bring that to people is really important and that definitely comes with your training as a statistician to think about being able to evaluate evidence like that. 

Alexander: Yeah. And communicate it properly. That’s very good. If someone else is now kind of intrigued about, ‘hmm never really thought about this stepping outside of statistics’, and things ‘Hmm, how should I evaluate’? What options are there? What truths recommend to someone like that? 

Liz: I said before, I think the key things to think about are what are you passionate about? What are you good at? And think about it in that wider context and where can you add value? Where would those skills that we’ve just talked about be able to add value in the organization? And don’t think about the functional structure that you have in your organization as a barrier to where those skills might be applied. So that’s first the thing is, think broadly, secondly, as we discussed, build your network, and think about the connections that you would like to have and how that will help you. Don’t underestimate those skills that you’ve got that we were just talking about. Those transferable skills that drug development experience that cross therapeutic area experience, you really don’t understand how valuable it is and actually how rare it is to have that drugs development experience. And the last thing I would say is leadership is just as important, not more important but just as important as those technical skills. Learning how to communicate and being able to bring a team along with you, enable a team, empower a team to be able to do it for themselves. Such that when you succeed they all come back and say, ‘look what we did rather than this is what you did’. It’s a leader. Having that empowering leadership. I think to me, the key thing is just being passionate about what you’re doing, building your network, you should have drug development experience and think about your leadership skills, as well as your technical skills. 

Alexander: Yeah, I once recorded an episode about this about technical skills and leadership skills are like we need two legs to run. And if you only concentrate on one or only on the other, you will not move fast, you need to have a good balance between both of them and then you can go over the fastest. So it’s about balancing these. I completely agree with that. Let’s say a more tactical next step to think about moving outside of statistics, is it that through this network you can get offered these opportunities or do you talk to people outside of statistics and say, ‘hmm I might be interested in maybe another career in regulatory or maybe a career in a leading projects or maybe in career in some other area.  How does it work? 

Liz: So I think there are two steps to it. The first step is having a really honest understanding of what your skill sets are and what your passion is. And sometimes that’s good to find a trusted colleague, maybe not in statistics, who will give you an honest opinion and reflect back for you. What they see is your skill sets when they see you in action, right? And sometimes they’ll surprise you with some of the things that they observed that you don’t really consider a skill, right? like so. So that’s really the first thing, honest evaluation of where your expertise lies. And then the second thing I would say is having conversations with people in those parts of the organizations. Just as you said, meet them for lunch or coffee and just have a conversation about their roles, what they are like, whether you’re interested in them and what their paths forward are. So I think to me, those would be the two key things, the honest evaluation and then the conversations with individuals in those organizations because you usually find people are very flattered to hear that you’re interested in being part of their organization. 

Alexander: Yeah, completely agree because that means they as a functional leader conveys some things that are attractive for the people working. That is really good. We talked about a lot of great stuff. We talk about what it is that you as a statistician makes you really really good and stand out that you maybe not even aware about. And there’s a lot of transferable skills, in terms of logical thinking, about data evaluation, about understanding the business of roles in development, communication with regulators and other stakeholders. This kind of end-to-end understanding from early clinical up to marketing and commercialization of products. And that is a lot of these things that can help you to find articles in statistics if that is your passion and that something that you want to pursue. And we also talked to a couple of tactical next steps that you can do specifically, building your network, evaluating options and getting this honest external feedback on your skill set. What would be your overall number one career advice when thinking about all kinds of different steps that you have been through and your long career within the pharmaceutical industry? 

Liz: I think if I could sum it up in one piece of advice, I would say, follow your passion. You’re always going to have more impact. You’re going to have more success and you’re going to have a bigger influence when you’re passionate about what you’re doing. There are those times when you really feel the flow of work when time seems to get suspended and it’s right on that cusp between how good your skills are and what is demanded of you. When those two things come together that’s when we really perform best. And when you can do that on something you’re passionate about, you’ll have a huge impact. So that’s why I would say, follow your passion.

Alexander: Follow your passion. That was awesome. And understand what your strengths are so that you can follow these. That’s a very, very good point. Thanks so much Liz for this outstanding discussion. I really, really enjoyed it and wish you all the best for your future career especially also with your Covid project. 

Liz: Thank you so much, Alexander. It’s been great to talk to you. I really enjoyed it.

Alexander:  This show was created in association with PSI. Thanks to Reine and Kacey, who help the show in the background. Thank you for listening. Reach your potential, lead great sciences and serve patients. Just be an effective statistician.