Are you a statistician looking to expand your career beyond the statistics department?
Do you want to know how your data expertise and strategic thinking can open new doors?
In this episode of The Effective Statistician podcast, I sit down with Swarna Khare, who made that leap and found success in strategic roles across the industry. We explore her journey, discussing how statisticians can leverage their skills to create a bigger impact outside of technical roles.
If you’re ready to take your career to the next level, this episode is packed with actionable insights to help you step beyond the statistics function and into new opportunities.
Key points:
- Expanding Careers: Statisticians moving beyond traditional statistics roles.
- Transferable Skills: Data interpretation, strategic thinking, communication.
- Career Transition: Swarna’s journey from statistician to strategic roles.
- Broader Impact: Using statistical skills in non-technical positions.
- Practical Advice: How statisticians can leverage their expertise.
- Opportunities: New career paths and roles outside statistics.
- Actionable Insights: Steps for statisticians to broaden their horizons.
In this episode, Swarna’s journey shows how statisticians can successfully move beyond the traditional boundaries of their roles and make a broader impact in various industries. If you’re looking to take your skills to the next level and explore new career opportunities, this episode is full of valuable insights and practical advice.
Don’t miss out—tune in now to learn how you can leverage your expertise for bigger challenges. And if you know colleagues who could benefit from this discussion, be sure to share the episode with them!
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Swarna Khare
Director – Global Integrated Evidence Planning at Novartis
Swarna is a statistician by training (MSc in Statistics from the University of Sheffield, PGDip in Applied Statistics from the Australian National University, and PGDip in Mathematics from the University of London) and she has worked as a statistical expert for almost 10 years. She started my career in various academic and research positions, focusing mainly on real-world data analysis and curation specifically in pediatric diabetes, neonatal health, environmental health, climate science, and various non-communicable diseases.
She then transitioned out of academia about 5 years ago to explore the opportunities to influence patient and public health through industry. She started as Senior Statistician at Almac Diagnostics where she worked with some most exciting, cutting-edge next-gen assays in cancer detection using advanced genetic data analytics.
She is currently working as RWE Manager at Novartis Pharmaceuticals UK where she ensures that we can fully leverage, often underutilized, real-world health data landscape to get patient-relevant insights into diseases and treatments by adopting innovative study designs and big data analytics. She is a dedicated lifelong learner and she strongly believes that if life never stops teaching, we must never stop learning. Her interests, outside of coding and programming, include traveling off the beaten path and exploring global culinary traditions!
Transcript
Can Statisticians Contribute to Moving Out of The Statistics Function?
Alexander: [00:00:00] Welcome to another episode of the Effective Statistician. Today I’m talking again with Swarna. How are you doing?
Swarna: I’m very well. Thank you. How are you?
Alexander: Very good. It is awesome. I checked the editorial calendar and it is quite some time that you have been on the show and we talked about how to think more strategically.
And why is that as important for statisticians? And I think you have brought that strategic thinking to lots of other places beyond statistics nowadays, because you have moved out of statistics, moved around the industry. So tell us a little bit about your career since, since you left statistics.
Swarna: Oh, thank you. Yes, it’s been a while since we spoke. Obviously, at the time that we had spoken, I was, I was kind of transitioning out of stats and exploring roles outside of just pure kind of biometric [00:01:00] pure statistics role. So at the time that we had spoken, I was covering partly real-world evidence, but partly also things like integrated evidence and other kinds of other business since then, I have moved into a proper kind of real-world evidence role, which is very, which is still fairly technical and still requires a lot of the statistical knowledge and the epidemiological knowledge of things.
But since then, I have actually then transitioned into real world evidence and Soon over the next few weeks, I’ll be transitioning into yet another role, which is going to be, again, very different from a kind of purely technical real world evidence kind of role. But, you know, it’s, it’s been, it’s been an interesting it’s been an interesting transition, but then, the one thing I would say is that as a statistician, it has really, all of the skills are so transferable, everything that you learn as a statistician, while you’re training as a programmer, as you know, whatever, whichever technical kind of expertise you have, those skills are super [00:02:00] transferable and they’re always.
It’s very, very helpful in whichever kind of direction you move, because every time an interviewer asks, like, you know, what is your key strength? My answer, my standard answer is, I have, you know, obviously good interpersonal skills, but the fact that I understand how to interpret data. How to actually present it in a meaningful way.
That is, I feel, is what clinches the deal. Like, you know, because there’s, there’s very few people who are good at, who are good at both. And I feel like statisticians, given the right training, can be, can be in that field where you understand the data, We can also understand strategy for the company. So it’s been good. It’s been, it’s been helpful to have been a statistician.
Alexander: That is awesome. I always kind of tell people about that, and since they should, shouldn’t take things for granted. Yeah. Just because it’s easy for them, it’s not easy for us. So let’s talk a little bit about these two skills that you mentioned.
[00:03:00] Understanding data. So. Could you contrast what is your experience of colleagues outside of statistics that are not statisticians with that background? Where do they struggle with understanding data that we find potentially completely normal and kind of don’t think a lot about it? Yeah. Absolutely.
Swarna: I think it’s, it’s really what we call the simpler things that really stump a lot of people on the other side who don’t have that statistical training. So, for example, I’ll talk about something which I think is a bugbear of all statisticians, like p values, right? So, you know, oftentimes statisticians themselves recommend against kind of presenting p values for certain reasons, because the data context is probably not right.
The question of interest is not the one that will. get addressed with a p value p value presentation, for example. But oftentimes with the minimal kind of stats training that people in the, on the other end, [00:04:00] mainly the audiences have, they’re very much used to things like looking at p values, looking at ratios you know, percentages, for example.
Whereas if, as a statistician, if I was to recommend against that, that, that is where a lot of people don’t understand why we would, why we would say something like that. Another example is things like hazard ratios, especially in pharma hazard ratios, incidents, risk ratios, things like that, which the interpretation can often be a little bit difficult.
For somebody who’s not trained in kind of formal statistics. So I think it is our responsibility as people who have been training stats stats to make it as simple and digestible as possible. So speak in English basically to the person who’s listening rather than telling them, Oh, this is a risk ratio without really, you know, Explaining what it actually means in terms of the context, but I feel like just the simple stuff that we take for granted.
Like you said, P values, risk ratios, hazard ratios, you know, models, the different kind of regression models, things like that. A lot of people get [00:05:00] stumped on that. And because they’re stumped, often they are senior people. They don’t also want to say that they don’t understand it, which is where like, you know, the clash of communications.
Alexander: Yeah. I think another area is when people talk about data, yeah. Yes. We, as statisticians sees a complete chain of data. Yeah. From the this report form or the claims database or whatever is collected up to the summary statistics.
And we understand that you, you can also create lots of other summary statistics with the same data. Yeah.
Swarna: Exactly.
Alexander: And Oh, you can look into other data sources, all these kinds of different things. So that’s very often not that quite understandable for, for others.
Swarna: Exactly. Oh, you’re a hundred percent right. I think when we say data, you’re right. I think, you know, you get a visual in your head suddenly, right?
When, when I mentioned the word data, I [00:06:00] get like, you know, tables in my head. I usually get the image of a raw data set. Usually one that needs to be cleaned. Other statisticians might get a different image, but that’s what I get in my head. And that for me is data. Yeah. But I assume that somebody in a managerial or strategic kind of role, when they think data, they think of the final output, like a chart or a table for them.
That is data maybe even written out. But for me, I’m, I’m thinking when you say, you know, get exactly that. When I, when I think data, it’s like this unclean, like, you know, Spreadsheet of, you know, raw data set for me, that is data, but yeah, you’re exactly right. When they say that, you know, yeah, the data is not ready, for example, they don’t actually mean that the data is not there.
It’s that the final output is not what, you know, is not what’s ready. For example.
Alexander: Ah, yes. That’s a lot of things about that. Oh, we don’t have the data yet. And you think like, Well, we have database, we do, we have the data.
Swarna: Exactly. Yes. Yes. That’s what I’m thinking. I guess we have the data. Yeah. It’s not [00:07:00] clean yet.
It’s not been analyzed yet, but we have the data. Oh my God. Yes.
Alexander: Yeah. The other is the communication of, of data. So You mentioned as statisticians, or as a statistician, you have been trained in that and you have put a lot of emphasis yourself on that. How has that helped you in roles outside of statistics?
Swarna: You mean the communication kind of training? Oh, it’s, it’s helped so, so much because I feel Alex, like, you know, when we, when somebody in a technical role and, and not even just stats, not even statisticians, let’s say epidemiologists or somebody working in real world evidence, for example, I feel like what we focus on when we are communicating data is the logic behind it, the methodology behind it, you know, the techniques, the details, which we are very much.[00:08:00]
Proud off because you know, we have taken this this raw data set and turned into this amazing, you know result. So for us, when we’re communicating that we often tend to start with all of you know, this was the background. This is what we did. This is the methodology. We really go into all of that back story first.
And then towards the final end of that communication, we get to it. This is the final, this is the piece of the story, what I found and through very hard lessons, I can, I can assure you it’s been hard lessons because I’ve had, you know, when you’re speaking to an audience, you can see when the audience is starting to disengage.
So I was in the state, for example, and I’m, I’m looking at my audience and some of them are not listening. Some of them on their phone, especially as I’m talking about my amazing model that I had put in place. And drinking water, not listening, like, you know, looking at the phones and I’m just like, okay, I’m using the audience.
And I think after those kind of initially almost embarrassing and difficult situations where, you know, you are, nobody is listening to you. I’ve [00:09:00] kind of learned the hard way that I need to start with, with the, with the final piece. At the start, and I would not, I would say that the training and communication kind of just practicing public speaking, speaking to different kind of stakeholders has really helped kind of drive that home because as I started doing that, I just started noticing how much people are more engaged because the Is that the start?
You know, if you if you look at the start, you’re not going to lose your audience. And that’s the one thing that they teach us in, you know, toast masters that I do my practice public speaking at is your title, for example, the title of your speech and the first few lines that you say. Will either hook your audience or you will lose them for the next 10, 15 minutes, whatever it is.
So this same thing for us, for stats and you know, epidemiologists that we need to start with the book. Like, this is what it’s going to do. This is what this data is going to kind of, if you’re looking to increase sales, for example, or drive prescriptions of something, you say, this is what we have found and this is how this can help.
And then you go back into how we got to that conclusion. [00:10:00] So I feel like statisticians are always focused on how And kind of the management and the other stakeholders are focused on the why and that, you know. Why did you do this versus how I did it? It’s, it’s completely different stories that we are saying.
So yeah, but that communication training really helped kind of for me to understand the why for my audience.
Alexander: Yeah, I completely agree. In university we learned this approach that kind of builds on, okay, where are the assumptions, where are you coming from, blah, blah, blah. And then we get to the conclusions.
And. In business, you know, like in you know, in magazines and so on, yeah, there’s a headline. Yeah. And a really, really kind of good first paragraph that basically tells the whole story and then you go into the more details. Yeah. Yes. Yeah. I could not find. And in the BBC or the Times or [00:11:00] any of these newspapers, you’ll never find an article that’s structured like a typical academic article.
Swarna: Yes, yes, exactly that. Exactly that. Like they always start with X out of, you know, 10 people, X out of Y people have, you know, this kind of cancer in the last, or the incidence of cancer has gone up by X in 10, 000 people in the last three years. That is such a major hook that, you know, we start with that.
If our data set, the analysis was, let’s say incidents of something. If we start with that, that’s hook right there. Right. I mean, can’t argue with, with a good hook.
Alexander: Yeah. A good hook can be a statement or a question or something that provokes. Some kind of reaction. Yeah. Curiosity is something like this. That’s always great.
Swarna: Yes. 100%. One of the things I learned at Toastmasters is there’s many ways to start your hook to get your audience hooked in. So you can start with something completely, you know, let’s say if you have a humorous speech that you want to give your, [00:12:00] your audience, you start with a very sad statement, for example, because people are completely not expecting that.
And that catches the attention and that you can repeat throughout your presentation or throughout your speech. It’s like, Everybody’s laughing and then suddenly you drop the mood and you just suddenly say, Oh, yeah, you know that my dog died, for example, and then everybody who is not listening, they will suddenly come back into it.
It’s a what? What happened? You know, we were just laughing a minute. It’s just you’re exactly right. Like getting the emotional understanding the emotional wavelength and, you know, playing with it. Just, you know, from a moment of, you know, of super kind of sad. Yeah. emotional speech or an emotional presentation and then you suddenly go into a humorous part of it.
It’s just keeping people kind of not in a monotonous way.
Alexander: Yeah, you could start a presentation about a study with all patients treated with this will die.
Swarna: Oh God, yes.
Alexander: And then say,
Swarna: yeah,
Alexander: but they will actually live much [00:13:00] longer than before.
Swarna: Yes. You know what? That is true. That is, you know, Oh my God, that, that reminds me what I used to do at all my previous jobs when I was like a full on statistician.
I always started my presentations with a joke, like a statistical joke. And so there was so badly. Terrible stats jokes. Like, you know, you’ve heard the one about, oh, you know a girl who has a crush on a boy and she, she goes to him and says, you know, how do I look? And he says, Oh, you are average. And she says, Oh, he’s mean.
I would put a terrible joke at the start. And everybody knew that was not going to be a boring, you know, old presentation about models. And even if it was, it still kept people engaged. So always stop found humor. Even if statisticians are not, you know, traditionally meant to be that way, I think that helps so much. So much. Yeah.
Alexander: Yeah. Yeah. Yeah. That is that’s good. And by the way, Chet GPT can help you with lots of, lots of different hooks [00:14:00] that you can create based on something you want to say. Yes.
Swarna: That is, that is true. That is exactly correct. And I feel like. I don’t know if you’ve heard of the, you know, the kind of the chat GPT engineering, obviously the prompt engineering, sometimes I struggle with that.
Like, I don’t know, you know for a speech, for example, that I need to give and I asked chat GPT, it comes up with a lot of great, like, you know examples of things that I can use, but feel like you really would need to use your brain a little bit to understand because chat GPT does not know who your audience is going to be.
But I, you’re absolutely right. Use chat GPT to get a start. It needs to be used in the way it was meant to be.
Alexander: Yep. Yeah. To kind of get some creative inspiration and go from there. Yep.
Swarna: Exactly. Get the juices flowing.
Alexander: Yeah. So in terms of the roles you have been in, so all of these roles had to do with the with data somehow, isn’t it?
Yeah.
Swarna: Yes, I would say definitely the current one is [00:15:00] very much data, data driven, data focused. The previous roles have been kind of a mix of, you know, understanding the needs for certain kind of data generation versus, versus not. But I feel like, as we were just talking before that you know, as as companies evolved, pharma kind of is now recovering from the post covid era.
There’s a lot more, you know, patients are now going to see doctors. Everything is coming slowly back to normal. A lot of pharma pharmaceutical kind of industry businesses are going back to the way things were before. So a lot of times statisticians and real world evidence For example, folk are not necessarily expected to work full on with data, but rather on understanding why we need certain data sets, you know, kind of building relationships with data owners.
So I would say that I’ve been lucky enough that my roles have always been, yes, they have been data driven, but there has been a huge element of all the other kind of accessories, accessory roles that you need to do along with that.
Alexander: How is, how do [00:16:00] you see the chances for statisticians compared to, well, non data people in these other roles?
Do you, do you think you have some kind of advantage or is it more of a disadvantage? What do you think?
Swarna: Oh, a hundred percent advantage. In my personal experience, it has been a huge advantage. When I say, Oh, I’m, you know, a statistician, I used to be a statistician, I was trained as one. That is a massive advantage because like I said, at the start, this skill of being able to understand data, being able to communicate it, that is a skill that is rare to find, like as much as we try to think, Oh, you know, some consultant from, I don’t know, some, some big, let’s say consulting firm, one of the big four, they can draw these amazing slides with, you know, amazing data sets, but really it’s the clients who need to understand that.
So it’s. Having that skill set of understanding what’s being presented to you and you’re not being taken for a ride, for example, I think that’s, that’s a huge advantage. It’s just going to be up to the statistician [00:17:00] to use it to their advantage. But I think intrinsically that advantage is, it’s unbeatable.
Alexander: Have you come across any situations that you really struggled a lot with?
Swarna: Oh, gosh. Yes. Yes. So again, going back to my stats days, like full statistician, I had just moved out of academia, which was, you know, very much in your,
Alexander: in your non stats times in my
Swarna: non stats time, as in struggled with the expectations that people had.
Alexander: Yeah.
Swarna: Ah, okay. To be honest, the expectations were kind of going backwards. So, you know, once you’re kind of established as, as an ex statistician or you know, somebody who has the knowledge, even if you’re in a non stats role, sometimes people still associate that with you that, you know, you still can understand all of this technical, you know, models and complicated programming.
But really, it’s been a long time since we’ve done that, you know. So, so I sit in these meetings where, you [00:18:00] know, people are, you know, presenting this very complex, let’s say, you know, multi faceted model. And they look to you to see if, you know, you can make sense of some of it. And I am as confused as the next person, because I think stats is one of those skills.
It’s like you need to keep practicing at it. So if you want to be a good statistician, you need to keep up with, you know, everything that’s happening. Once you’re not a statistician, I think the focus is really on, on the other aspects of your role. But I, what I have really struggled as a non statistician is kind of being associated still as having those technical skills.
And I, I really lost touch, to be honest, I am.
Alexander: Okay.
Swarna: Yeah.
Alexander: In terms of expectations versus yourself. Yeah. So have there been situations where you were scared about, kind of you were asked to do some things that scared you?
Swarna: Yeah, I think, I think it’s, it’s almost a, it’s almost a [00:19:00] daily phenomenon, you know, you always face these these new situations that you have to present to some very senior leader.
Yeah. And you are kind of battling between what is it that I want to present to this person. And those are the situations because it’s, it can usually be a hit or miss, right? The other person on the other side, you don’t know who it is, right? You just know of this big boss, somebody up there who wants to know what you’re doing.
And you try your best to estimate what they want to hear. So that’s something I try to do before the meeting or before I start my presentation is try to understand what they want to hear. Okay. So if I’m sitting in a, in a meeting, but I have to present my little work, you know, my little project to the senior people, I’d always again, think about what they want to know.
They’re spending a lot of money, you know, contrary to popular belief, real world evidence is not cheap. So, you know, it’s this quick and dirty is not true. It’s not true for real world evidence. So they’re sitting there paying, you know, a few millions for, you know, a secondary database study. Let’s say, I think that if I’m [00:20:00] presenting to somebody like that, My fear is, am I giving them what they want?
And I always try to estimate thinking that they’ll probably want to know why am I paying all this money for this work? So I, my job is to show them that if you give me, you know, let’s say 4 million for a study, how is that going to generate revenue or drive the sales of your product? 10 folds, 20 folds, whatever.
So I, my, my fear is I want to make sure that the impact of my work is coming across. And if I don’t break out of that mindset of the technical person of the stats person, I’m going to lose that opportunities. And those opportunities are very rare. Like you get one chance, right? So I would say for me, that fear of not being able to communicate properly, to not being able to succeed in that, in that communication is, is it’s every day, you know, you always feel a little bit imposter inside, but you know, you, you give it a good shot.
You try and estimate what it is that the other person is. going to be interested to listen to.
Alexander: Yeah. I think that is a, [00:21:00] anyway, a great advice for any communication. Yeah. Always start with what’s in it for the other person. Why should they care? Yeah. Yeah. What, what do you bring to the table? Yeah. Yes. These kinds of things are really, really important.
And I’ll talk about exactly that in our, our leadership program. And it’s it’s one of the absolute basics. And so often people more think about themselves. In presentations. Yes. Rather than about the audience and in this case kind of the senior decision maker. What they want to know.
Swarna: Exactly. It’s so contradictory isn’t it?
It’s like, they say, oh you have to go and present. And we take that opportunity to present our work as an opportunity for us to shine somehow. Like, you know, this is our one chance, you know, we become visible to our seniors. But really, I think that’s such a loss because, you know, they’re not really interested in what you are, what you’re doing is really how your work is going to help me.
If it’s a salesperson, they’ll want to know [00:22:00] driving numbers. If it’s an access person, they’ll want to know how will this help me get access. So it’s, it’s really all about the audience, but yeah, we, we sometimes get so mistaken about, you know, this is all about me and to make ourselves shine, we put in all our models and all of our work.
It’s a waste. It’s a waste.
Alexander: Yeah. Yeah. I completely agree. When you think about stepping outside of statistics. How has that helped to progress for you from a career point of view?
Swarna: I think it has definitely opened up a lot of doors, which were, you know, which initially I thought were not meant for me. So, for example, kind of looking at things like you know, Integrated evidence.
Let’s talk about that because I’ve done a little bit of that in my previous roles initially as before before when I was, you know, still a statistician, always felt that those kind of roles, you know, strategic managerial roles, kind of very theoretical in [00:23:00] their in their being. Those would not be the kind of roles.
Not only would I not enjoy or those will not be open for me because I won’t have those skill sets. But like I said, I With me, it’s always been and this is an advice I got once when I was at university. My, my supervisor told me, if you like a job, just apply for it. He was like, don’t look through the job requirements.
Look at the job description and then apply. So I followed that advice and I actually started putting myself out there for roles, which I thought I was not qualified for. And turns out, like I said, the skills that we have as statisticians, they’re extremely transferable. And sometimes we just put limits on ourselves saying, Oh, I will never be able to do this kind of role, or they will not consider me, or I don’t have the skillsets.
That is a complete fallacy because as statisticians, we are actually in a very strong position. Compared to many other people who, you know, who don’t have the technical background or training.
Alexander: Yeah. What have you enjoyed most as outside of offset from a kind of, you [00:24:00] know, personal experience point of view?
Swarna: Yeah, that’s, that’s a good question. I feel like the thing is personally, I enjoy. Speaking a lot like public speaking and communication. So for me, having those opportunities to present my work, present myself to kind of more senior stakeholders and not just senior stakeholders, but kind of cross. Cross enterprise stakeholders.
That is something I have enjoyed a lot. And I know people would probably think, Oh, come on, you know, who likes doing presentations, who likes speaking, but I actually have enjoyed it mainly because I see it as a scientific thing, I don’t see communication as, you know, something that’s just humans do, because I’ve spent so much time kind of training myself to become a better communicator, like, you know, with dose masters and with other, many other kinds of speeches and things.
I actually see it as a very scientific process. So for me getting stuck into kind of communicating with others, not just in meetings, but even in one to ones and understanding what it is that the other person needs from me and [00:25:00] then putting that in place. I have really, really enjoyed that. So for me, it’s never, it’s been about not putting myself first.
It’s about putting others first. And I have the way I’ve seen, like, you know, people’s kind of eyes changing a little bit, like, you know, becoming that a little bit bigger when you’re speaking to them, because they’re like, Oh, I can use this. You know, there’s suddenly something lights up in the head. I love seeing that.
I love seeing how I can add value because that, you know, at the end of the day, I’m just like, okay, at least I helped somebody today. So I have definitely enjoyed that aspect the most.
Alexander: Yeah, awesome. However, I’m pretty sure you haven’t always enjoyed public speaking. Oh, God. Yeah.
Swarna: 100%. It’s you know, there’s a saying that says what is it?
This is when I had first joined Toastmasters, and they were telling me that, you know, people would rather, they had interviewed a bunch of people, like in a study, and they’ve asked them, what is it that scares them most? On top of death. So it was [00:26:00] not even death was not even the first one. It was public speaking, followed by being in a coffin.
So that really opened up my eyes because the first time I did it, I was shaking. I was shaking on the stage and it was just a two minute thing. Somebody just asked me to come up and just, you know, get comfortable to be on the stage and be the center of attention because whenever you’re speaking, the spotlight is on you.
And a lot of us are not comfortable with that. So I think it’s not even the fact that speaking is unenjoyable. It’s being the center of attention, being in the spotlight. So what, what, what I started doing was if somebody invited me up, I would just go and stand in the stage and say nothing, just get comfortable with like 20 eyes, 30 eyes all on me.
And I think that’s what scares people the most because you’re being watched by so many. So it’s not even the speaking or your content. It’s just getting comfortable being the center of attention. And as statisticians, sometimes as technical experts, we are very happy to kind of be in the background, you know, be part of the crowd [00:27:00] rather than stand out.
So I think if you get comfortable with that, that’s okay. But yes, I have not always enjoyed it. It definitely not enjoyed being the center of attention.
Alexander: Awesome. Thanks so much for sharing that. That means. a lot to many people, yeah, because as you just said, many people would rather die than speak publicly.
So, and that is not things that you can’t break. Yeah. Yeah. I many, many little steps pushing yourself. Yeah. Giving more and more presentations to bigger and bigger audiences. Yeah. You can turn that feeling around from really, really big fear into something that is that is enjoyable. That is fun.
Yeah. And I completely agree. If you, if you see people and say, Not with your head and say, kind of, you really sees as these light bulbs go on. [00:28:00] Yeah. That is super rewarding. Exactly.
Swarna: 100%. If you know your whatever your content is, it lands, whether it’s a professional setting or whether it’s just, you know, casual communication setting.
Once exactly like you said, if you see what you’re saying, it’s landing on the other person’s kind of radar. It’s, it’s excellent because that’s, that’s the job done. Right. Do it.
Alexander: Yeah, yeah. Do you think as a person outside of statistics, you have more channels, more platforms to reach a wider audience?
Swarna: To be honest, I feel like Probably not.
I feel that statisticians and, you know, technical experts have kind of equal opportunities. Oftentimes we turn them down. I feel because even when I was working as a statistician, I would be very happy for somebody else to present on my behalf than me doing it for myself. So I feel like the opportunities are more or less similar for, for kind of either [00:29:00] statistician or non statistician is just, do we, are we good at taking those opportunities?
Are we good? Like you said, at saying, let me do this, you know, and oftentimes we’re not very good at doing that. And if there’s nobody to blame in that, it’s just a lot of times it’s personality driven as well. But I would not say that non statisticians don’t have as much opportunities. It’s just, we tend to just give it up and be very happy for somebody to put two slides on the stats bit and then present it, you know, without us coming forward.
I feel that’s more the case.
Alexander: Yeah, I think there is. And because of that, people kind of step back, that becomes kind of a habit and an expectation. Yeah. Yeah. Mm hmm. So you said, oh yeah, the non statisticians will talk about all that because the statisticians don’t want to speak up. Yeah. Yes. And from that, it becomes.
kind of de facto rules that some non statisticians present today. [00:30:00] So, and then it becomes sometimes really hard for statisticians to break through that. Just because so many others Before them or in their organization, don’t speak up.
Swarna: Yeah, exactly that. And it’s, it’s such a miss opportunity there because we have that, but also I feel like as the reputation of statisticians changes in, in big Pharma and big industries anywhere, they are.
Kind of the invitations are forwarded to them to them to, you know, the invitations to speak are forwarded to statisticians. We either don’t take it or if they’re not coming to us, if the invitations are not coming, it’s probably because of the reputation that statisticians have that they’ll just focus on the models and the details.
We don’t really want to know about all of that. So I think somewhere the responsibility is also on us as technical experts to change that perception that, you know, when we send out pre reads, for example, we don’t put in like a bunch of. [00:31:00] Technical details about how something was focused really on, on the pieces that are impactful, and I think once somebody sees that, then why would they not invite us to speak?
You know, it’s just the responsibility lies also with, with the experts.
Alexander: Okay, cool. So how do you have experienced working with statisticians as a statistician in the non stats space?
Swarna: Oh my God, that is such a funny question. Yes. And it’s a very valid one as well. And I feel like my respect for stats has gone up so much.
Not even when I was a statistician, did I think of their thing of the importance that I bring to a project. As I do now, like there are so many kind of real world evidence studies, real world data sets where I would not, I would not even think of setting up a project right from the start without having my stats partner with me.
It’s, it’s, you know, my perception of [00:32:00] myself as a statistician when I was one was completely different to how I’m seeing, how I’m seeing others. And I think that’s a good point for statisticians to know that we often tend to underestimate our own impact in, in project meetings and in. kind of bigger, bigger, bigger teams.
As an ex statistician, I can say I would not, I would not touch anything to do with data or with technical without a statistician present. So I think for me, my, my respect for stats as a, as a, as a core partner in any kind of meeting, any kind of project has gone up like 10 folds, even more than when I was one myself.
Alexander: Awesome. That is so nice. But that hopefully boosts the confidence of statisticians out there. As I think that is often one of the reasons why people don’t speak up. Lack of confidence is definitely something. Yeah, exactly.
Swarna: And the more that stats kind of the statisticians and more kind of SMEs and technical experts kind of move away [00:33:00] from, you know, just purely technical roles and they move more into management side of things.
It’s also their responsibility to make sure that, you know, how they feel about stats is then translated into practice that, you know, they, they definitely demand that any new project must have a statistician from the, from the outset, right, from the bat, instead of like, Oh, now we have bought the vendor.
We have understood the protocol. Now we’ll have a statistician. It would not work that way. So I think it’s more of us kind of start, you know, we’re getting older. We move into more senior positions. It’s, it’s definitely our responsibility to make sure that, you know stats is represented appropriately.
Alexander: Yeah, I think it’s a, it’s a great strategy. Yeah. Develop people so that they can also leave statistics, join other functions within the organizations and that will help that data is better understood. Yes, leverage says better decision making based on data, all these kind of different things. So from [00:34:00] my point of view there can only be good things happening if we get more statisticians to work in other areas.
Swarna: 100 percent that that’s, that’s going to change a lot of things. And like you said, it’s to develop that kind of, you know, that thinking about the importance of stats that can only happen when statisticians kind of put themselves out there and say, look, I’m eligible for this role. I’m actually going to be very good at it.
They go ahead, apply, and they get those roles. And then it’s, it just changes everything because if, if somebody who has never been a statistician then becomes a manager of, you know, let’s say real world evidence teams, they will never see the importance. They will never know the importance of stats. So, I think it’s it’s time statisticians kind of rise and say, you know, get their confidence kind of back.
It’s, it’s so needed. So needed.
Alexander: And statisticians can climb up very, very high. So I, I have seen the chief information officer, yeah. Of Lilly.
Swarna: Oh, wow.
Alexander: Used to be a [00:35:00] statistician. So she was directly reporting into the CEO. Yeah. And had all IT and all these kinds of different functions under her. Yeah.
Or there’s another CEO that I know that also actually, actually became a CEO of a biotech company. Yeah.
Swarna: Wow.
Alexander: Yeah. Wow. So, so we shouldn’t say as statisticians, we can’t do these things. There are role models that, that have very, very success, successful careers outside of statistics.
Swarna: Yes, absolutely. And, and anybody who can make that switch from technical to management. Whoever can make that switch successfully, there’s the world is their oyster. Like nothing, nothing can stop them because they have the best of both worlds. Like, and, and that makes a unicorn, you know, you don’t get people like that very many of them, so yeah.
Alexander: Awesome. That is a very, very good final sentence. If you combine these management, these people skills with your technical data [00:36:00] skills, you’re really a unicorn. And so that is. will give you lots of opportunities. Seize them. And as you said, you know, even if you think you are not qualified, that’s just your perception.
That’s not the reality. Yeah. And so ask for it. Yeah. What can happen? You may get a rejection here and there. That’s it. Yeah. If you don’t ask, there’s a no by default. Yeah, exactly.
Swarna: It’s a no by default. A hundred percent.
Alexander: Yeah, yeah. Awesome. Thanks so much for this awesome discussion about what statisticians can actually achieve if they leave their comfort zone, also biostatistics, biometrics, data science departments, and how much we can help them.
Our overall organizations to become more data driven, have better decisions, communicate [00:37:00] data better, and see the, all the different opportunities that we can do with data.
Swarna: It was a pleasure talking to you, Alex. It’s, it’s always a pleasure talking to you, but you know, this, this was a very appropriate discussion and I feel it’s at the time is right because, admin of kind of AI developing so fast, you know, statisticians and anybody with a technical background is in such a great position.
We just need to realize it and know that, you know, there’s a lack of such skills. You, you have to go out right now. This is, this is the time to really seize that.
Alexander: Thanks so much.
Swarna: Thank you.
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