Biopharma Companies Wrestle with an Outsized Demand for AI Talent

Daniel Levine (00:00)

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Amar, we've got Steve Swan on the showtoday. For people not familiar with Steve, who is he? He is the CEO of the SwanGroup, which is an executive recruiting firm for biopharma companies. The firmspecializes in recruiting people for positions that require data, data science,machine learning, and AI background. He's also the host of the Biotech Bytespodcast. And what are you hoping to hear from Steve today?

I'd like to see what insights he can offeron the supply and demand for data analytics and AI talent across the healthcareand biopharma, and how the rapid advances are changing the talent needs forthese companies. I'd also like to see whether companies are sticking tocandidates with very specific industry backgrounds, or if they're having tolook outside the industry to find the talent that they need. Well, before webegin, I want to remind our audience that

If they want to stay up on the latestepisodes of the Life Sciences DNA podcast, they should hit the subscribebutton. If you enjoy the show, please give us a like and let us know yourthoughts in the comments section. And an audio only version is available onmajor podcast platforms. With that, let's welcome Steve to the show. Steve,thanks for joining us. We're going to talk today about the growing use of dataanalytics and AI,

how this is affecting the demand forspecialized skills in healthcare and biopharma, and what companies are doing tofind the talent that they need. So let's start with the big picture. How hasthe demand for AI and data analytics talent in healthcare and biopharma changedover the past few years? Well, first of all, Amar, thanks for having me. Iappreciate it. I'd say over the years, I mean, you know, we started, I mean, ifyou go all the way back, right? I mean, I think, you know, you and I have hadthis discussion,

I think once when we got together, talkedabout how maybe it went from big data, right? Then it went into analytics. Nowit's into AI, right? So I think the way it's changed over the years that I'veseen is more the tool, right? More the tool that folks are using and more whatthey're using to crunch their numbers, whether, back in the day and still to someextent today, they use SAS, right? But I mean, now we have

algorithms, right, that are going to do theAI workforce and such. But, you know, I mean, it's always been about the data,Amar, you and I talked about that, right, for all these things. And we'recontinuing to, in my opinion based on what I see in the needs for new hires andsuch, struggle with that, right? I mean, you know, we really need to make surethat our data is in shape. But I don't think over the years it's changedsubstantially. I think we've changed the names. But I think the generalpremises are the same. And like you, again,

Like you and I talked about in the recentpast, I think it's about frameworks and really thinking about everythingproperly. Okay. Are you like, was there like some point of inflection that youcan think about, Like where, you know, the demand has been growing for dataanalytics. Let's say if you take last 20 to 30 years, has there, has the demandbeen just growing gradually or have there been like points of inflection wheresomething new came up and then there was just a

lotof demand right away after that as the industry pivoted. Well, if I had to say,I'd say, know, data science really became a thing, right, say 14-15, 2014,2015. Okay. And then it really started taking off the demand for the analyticsand then AI. I mean, you know, right? So with the amount of data that we haveand the amount of ability to store it, retrieve it,

slice it and dice it, that really startedgrowing exponentially. Like I said, 2014, 2015. But it's a problem still withthe amount of data that we have. On my podcast, as I talk with a lot of theCIOs, and I think there's some of the same folks that you chat with, again,going back to my comment from my first answer, they can make that shiny object,right, to do some of those analytics and things. But it's more about...

that data and what the input is and howyou're going to make that make sense, right? So I think with the increase inthe ability to store and hold on to all that data, the inflection point, if Ihad to put my finger on it, really started ramping up in 14 and 15. Then I'dsay exponentially over the last two years with AI. You know, in a big way.Okay. And do you see the demand spread through the industry or are there like specific

roles that are in highest demand withinsome of these domains? So there's specific roles, think, know, commercial andclinical,  especially, right? You and Italked about that, I think, at some point. You know, the commercial stuff isgoing to be more linear, right? So the commercial is going to be, only one timea year you're going to do an IC plan or something. But when you look at themarketing side of things, right, that's where you really get a lot of thedifferent data. And then on the clinical side, same thing. If you start

pumping in their EHR and EMR data and allsorts of different things, you're getting things from many, many differentangles. So the different roles that I see that are out there are folks that dounderstand some of that data because in order to know what your output isyou're looking for, really where you're going, you gotta know what's going in,right? So a lot of the companies really are looking at some of these folks thatunderstand it. Now,

some of the leaders that I talk to in thesecompanies are starting to, again, acknowledge their data shortcomings, right?Because they've got 20 and 30 years worth of data and they gotta get backthrough it, right? And so they have their, whether it is directors of datascience or analytics or even AI, right, that are doing their number crunchingand looking at all this stuff. They tell me that they're getting frustratedbecause

some of these leaders are doing datatriage. They've got to, as they're going through and doing their thing, they'vegot to deal with the data, which you don't need to be spending what you spendon that person to do triage on the data, although you do probably need thatperson in good proximity to the person doing the analytics, right? You probablydon't want to wait a half a day and have somebody on somewhere else in another,East Coast, West Coast, whatever that is, to do that. You probably wantsomebody that can help you

triage that data at a lower cost and that'sreally what they do. So my solution and my comment to those folks was, getinvolved with a lower level data scientist maybe right out of school or with ayear or two of experience, they'd be glad to do your data triage and they'dprobably be pretty good at it. You're not paying them as much as you are yourdirector and your director and associate director can keep doing the higherlevel tasks they're doing. And then that junior level person can then...

get up to speed. So, it's basically as, Imean, the way I think about it, right, like when you look at some of thedifferent type of roles here, right? So you have on the one side to put thedata in one place, right? You have data architects, you have data engineers,right? We're putting those things, data together. Then you have the datascientists who are like these machine learning experts who are coming with thealgorithms.

But then you also need these, first of all,you need some of these ops people, operations people, data ops or machinelearning ops. And then you all have this analyst, the business analysts who areactually doing that. So these are the different types of people you need. howdo you, so from what you're saying is that yes, there is hiring, but are peoplerealizing more that, okay, well, the data needs to be in place for the otherpeople to actually operate. So we need a lot of people,

or like data architects, data engineers toactually get the data in shape? Yeah, absolutely. I think that, well, I know,I've seen studies, 70 % of these projects actually fail. And the reason beingis because, in my opinion, based on what I'm reading, that we're putting thecar before the horse, right? So we're going out and a lot of the IT folks aregetting some pressure from the business side, whoever that might be, to buildout some great stuff that we can use and...

make money with, right? Improve ourprofitability, which is absolutely a noble cause in what we're supposed to bedoing. However, you know, we go ahead, we put the car before the horse bybuilding that technology, right? That engine. But we don't have the gasolinefor the engine ready, which is the data we're talking about, you know? And youand I talked about that, I think, one day over lunch, right? It just gets, youknow, it gets crazy where, you know,

and on some of my podcasts, if you lookedat any of those, all those CIOs at these biotechs say the same thing, that thedata is just, for the most part, isn't there, it's not ready. They can get itready, we can get it ready, but it's gonna take a lift, right? And so to yourpoint, if you put that team in place, right? The data engineers, the dataarchitects, da da da da da da, you know, the DevOps, right? All those folks,you're gonna need all that in place to automate that cleanup and to get thatdone. Yeah, yeah. And in terms of like the different domains,

on research, clinical development,commercial. So you're seeing the demand across the board or are they in aspecific domain where you're seeing a lot more demand? Which domain am I seeingmore demand in? And I'm asking you, is there like specific domain where you seemore demand? So if you look at let's say research versus clinical developmentversus commercial, medical affairs, like are there specific areas where thereis like the

there's a lot more demand for data AIpeople, or is it kind of like spread across the board in... I'd say on theclinical side is where they've applied it most, right? Okay. Where they've hadthe most success, where they've utilized these folks and where they've beensuccessful, and that's going to continue, right? I think the research side isthe holy grail, you know? Yeah. In order to shorten that cycle. I mean, youknow, if you look at these CEOs that get skewered on C-SPAN because of theprices they charge,

they all default back to their 10 yearresearch cycle or whatever that number is. And if they can utilize AI to lopoff six months, a year, imagine, I mean, that'd be great. But right now theclinical has so much data and so much stuff around it, it can already use AI tokind of help that whole process out, automate some of it, do things a lotquicker than we can staring at it with our manual programs, which I mentionedearlier, a SAS or something like that, right? Okay, okay, okay.

See, now the demand is increasing quite abit, supply, there is a bit of a shortage. So are companies primarily lookingfor candidates with the pharma industry experience? So are they open to talentfrom other industries as well? So they're open to talent from other industriesat the lower levels, at the more senior levels, they're going to look for thespecific domain knowledge, right,

when they want that person that's gonna...If I can just go back a second, I think that the industry, because I only knowthis industry, our industry in particular when it comes to technology folks thatthey look for, got very, very focused on we need exactly this, right? So that'scontinuing in this field as we go up the ladder in seniority. I've been doingthis 25 years and that...

started like I said maybe about 10 yearsago that started. 20 years ago wasn't that way you know but it's gotten a lotmore specific and AI and data analytics and such has only increased that thatneed and that drive and that you know requirement if you will right?

So that's interesting, that's why thenewbies you can get from different industries, at the leadership level, thefarmers want some experienced people to provide the right guidance then.Absolutely. Yeah. And they want the folks that do have the technical as well asthe business knowledge. You know, some of them look for, I've seen folks lookfor

sometimes scientists first, IT guys second,you know? Where they got into science and they knew that they wanted to do X,Y, and Z, but they're pulling their hair out. How do I do this? Well, theydevelop their own technical solutions and then they stumble into one of thesefields, right? And then lo and behold, they're really valuable to the rest ofthe guys and girls sitting next to them because they can do stuff real quicklywith either their automation or their technology that

they couldn't do three months, six months,a year before. So lots of times at this more senior level, it is business side,science first, and then technology second. Not the data analytics AI. Has that been growing more in IT or also inthe analytics or data analytics functions within the business? Because there'susually the IT and then the business function, right? And then there's thebusiness. So it's basically IT,

the business data analytics, and thenactually, let's say marketing or scientists. So where is this growing and howare they working together? What are you seeing there? Well, so you got,sometimes you got the technology folks in business, right? Sometimes you gotshadow IT in the business, and then sometimes you got legitimate IT. And thenalso when it comes to the data, the business still owns the data for the mostpart,

and then the platforms and such that theysit on, IT is owning and running and figuring it out. So I've seen it on bothsides and I get the calls for both of those, whether it's in business or IT, becauseyou're not gonna, I don't know, you're not gonna call somebody that specializesin commercial sales folks for their AI person that's in the commercialdepartment, right? It's just not, they're not gonna be able to handle that,right? So likewise for the data side of things, right?

When they're working on data and the dataplatforms, I mean, you someone's got to own the data and work through the dataand handle the data. But as far as, you know, again, going back to the DevOpsfolks and all those folks that are handling all that, they're IT folks. Theytoo are going to come to me. So I see it in both sides of things, both businessand IT, but for the most part, I mean, you know, the IT folks are going to ownthose data platforms. For the most part,

they're going to be, the analytics folksare going to be on the business side and the AI folks can kind of be on theother side, just depends, you know? Okay. Okay. Is there like a specific modelthat you've seen working better than the other or like, are any... You mean asfar as on what side it belongs on? Yeah. Yeah. Like the talent, right? Like asyou're hiring these talent and then you're looking at how these specific groupsare

doing, you know, whether they're doing wellor not, like, is there like specific operating model that you've seen beingmore successful than the others or is it like, it just depends on people more?Well, so the one that I've seen that works best from where I sit, right, is ifthe folks that are doing the analysis, the analytics and the AI work thatthey're looking for somebody to do actually sits in and reports to business,right?

Okay, okay. So more like the dataengineering, data architecture, DevOps, those more on the IT side, but thenmore like the insights, analytics, then using, let's say, genAI to get more ofthe insights on that, more on the business side. Right, yeah, absolutely. Andthinking about it, as you just summarized that for me, you're right. And thisis the first time I've thought about this and articulated this, is that

if that's the case, right, then that couldbe part of the catalyst as to why our data's in not as good a shape as it couldbe in, right? Because if the data platforms and all the technology, the DevOps,sit in IT and IT is getting a little bit less attention or a little bit lessresources or a little bit less ability to handle that, and they've got all thisother stuff, their day to day that they've got to do. That's more proactive.

So maybe they're not given the budget. Idon't know, this is all speculation. Maybe they're not given the right budgetto do all that. Maybe that's part of the reason why we're behind a little biton that data. But I don't know. Again, just to, you know, all the CIOs thatI've interviewed though have said that the data, you know, they've got to getthrough a lot of data and it's real tough to do, you know. Okay, okay. Now, arecompanies interested more in people with the IT background or with the businessexpertise? Or does that depend on like--

How does that depend on the roles? It doesdepend on the role. Like I mentioned earlier, you know, lots of times they'relooking for the scientists first and the, you know, the technology second. Thatwould be on the research side, right? The clinical side, I mean, you know, aslong as you've done some of that work before, right? Like, you know, the manualSAS work or you really got some great statistics, so that's a lot going onthere, And then under the commercial side,

they're not too worried about it if you'redoing analytics on the commercial side, whether you know what the territoryalignment is and all that fun stuff, right? That seems to be less so. That'smore,  do you have the chops to do whatwe're looking to do? Maybe they would look for somebody that knows the data,the data sets. So that data would tend to come from an IQVIA or an IMS or aSymphony, you know, that kind of one of those places. Okay. Okay. And is therelike a typical level of...

education that employees are looking forfor the specific like AI or data analytics role? I'm talking more about likethese data analytics or data scientists AI type of roles rather than just likethe data roles. But like is there something that they especially look for? Ithink from an education perspective, they're not super particular, but I'lltell you all the best ones that I've found and all the best ones that I've seentend to have their masters. You know, they've got their masters in datascience, whether they went to, I'm not far from

Lehigh, Columbia's got a great program. I'vebeen told that NYU is not super great, but I had a kid that went to NYU, youknow. But yeah, but there's some programs that are better than others, youknow. I think Pitt's got a good one that are few more favorably than others.But for the most part, the ones that get those higher level roles and thosegood roles tend to have their masters in data science. Yes. OK. OK.

Now, you mentioned that in the last coupleof years with this rise of generative AI, there's a lot of demand for people.But do you see that companies have thought through in terms of exactly whatthey want to - who they want to hire, what kind of profiles, what they want todo? Is that something clear or do you think that companies are rushing to justget some of these people and then they'll figure it out? Omar, it's so all overthe map. It really is. I mean,

I can get a call from somebody that knowsexactly, I need this, I need this, and perfect, let's go, you know? Just likeAI, you gotta tell it what you're looking for, you know? And it'll get there.But then I have had the calls for, you know, the first question is, what are wedoing? What's the overall goal here? Well, you know, if I hear from somebody,well, I just wanna make sure I have somebody doing this kind of work, because Idon't wanna miss out or whatever. The whole FOMO thing,

I can't get involved because that's nowthree, six months. If that doesn't work, that's my fault. Right. And I don't, Idon't, you know, I, I, I, I, I do well enough getting myself in trouble. Right.So, I don't need help, but, yeah, you know, it's kind of all over the map wherethey are on their, you know, on the scale of we need this to, we don't reallyknow anything that we need.

It is changing a little bit, you know, andI think you're probably getting to this, but it just popped into my head of anyof the latest trends, right, in AI. The biggest one that I see right now iswe're all trying to get to the point, or well, I guess we're all scared of AIthinking and reasoning and taking us, know, Arnold Schwarzeneggerish, right?But to get the algorithms closer to reasoning, if you will, in the currentcomputer

paradigm that we're in, they're starting to- the latest trend that I'm seeing is they're starting to basically apply highschool math, algebra and calculus to prove out these theorems, to prove outthese algorithms, right? And there's even some tools that can help with that.So some of these AI companies that are only involved with AI are hiring some ofthose folks and are looking for some - very, very few people are doing it rightnow, but it's to help,

again, to help these algorithms get betterat what they're doing. So building the algorithms, fine tuning them, thenproving them out with math to get them better at reasoning. Now, the folks thatI talk to that do that are telling me that, you know, in order to really getthem to reasoning, we got to change our whole computing, you know, paradigm toneural net. You and I kind of went into that when we spoke in the past, whichwe're not at yet, but this is going to help our algorithms, this whole provingit out with the...

or math to help them get to a betterreasoning place. How it actually works, I'm not the guy or girl that does it, Idon't know. That apparently is the latest, latest thing literally over the lastmonth or so. Okay. Now, do you see these pharma companies developing the basicsof the algorithms themselves, or do you see them delegating that to the vendorsand then them

like more focused on the application ofthose in their specific business problems. So I see them doing it themselves.There's some progressive ones out there. can name, I won't name them here, butI see some progressive ones that are doing their own, they're building theirown algorithms. And they even, some of them have gotten to the point wherethey've spun out and they're like, wow, this is pretty cool. Let's create thislittle side company or whatever, to do this.

They don't seem to be relying much on thevendors to do that. But it brings up a point that I've had with some of my CIOsis that if I build my own algorithms, right? And I do my thing with myalgorithms and building out my AI, where does 21 CFR part 11 fall intothat?  I mean, how close am I getting tothe FDA validation need on that? I don't know, Steve Swan doesn't know. SteveSwan's never gonna solve that, but,

you know, it's, it's, it's a thought and,I've had a couple CIOs or other folks even say to me  that they could see each company having itsown sort of stock algorithms, you know? And I said, well, if it's a stockalgorithm, it's going to be solved for the same problem all the time, right?Maybe I'm wrong, but, then you validate that once and you're done, you know? SoI don't know. I don't know. But yeah, they're not relying a whole heck of a loton the, vendors that I sent

to build those out for them. I see themdoing it a lot themselves. It seems that they can build that easily, That'skind of like what they're saying. I think we're going to see some of the morechallenges about how do you implement it enterprise-wide and stuff like that,right? So I'm not sure how feasible this model is of everyone building theirown custom algorithm because you need a lot of those, right? And then do theyhave the kind of manpower to actually continue to build that, right,

as changes are happening in GenAI are theyable to sustain that, right? Well, no. And I'm going to just say, no, theydon't. They can't sustain all that because the need's growing. The database isgrowing, which we just talked about. And there's a run on the - somewhat of arun on the market for the talent. So everything's moving in the wrong directionfor that to continue, right?  Yeah. Yeah.

Yeah, yeah, absolutely. Yeah, so let'ssee.  I guess no one knows right now,right? It's about how this is all going to shape up in the next two, threeyears. They don't. They have no idea. But, you know, the wild thing about this,and again, in the past, I think we talked about this, how quick this all moves,you know, like these, these  proving outthese theorems, the math is - I hadn't heard about this. And then all of suddenI started hearing about it. Like that's like the newest, coolest. And obviouslysome folks are doing it and loving it and having fun doing it. But

You know, it's just, this thing's flying,you know? Now, kind of flipping the coin, we talked about these companies wantto hire people, and especially junior level folks, they're open to hiringpeople from other industries. But are AI experts willing to join pharmaindustry when they have a lot of opportunities in, let's say, tech sector orlike,  finance industries? Do you see

them joining easily or is there a lot ofreluctance in these people for joining the pharma industry? Reluctance. And,you know, they're not sure what that's going to do for them, you know, as theyjoin the pharma company because the AI experts for the most part tend to be at,you know, a lot of those maybe more sexy tech companies, you know, so they seeit as an older industry and stuff. But again,

some of these companies have made theselittle spin-outs. So those AI experts would join those and maybe that's theirsolution, right? Because if you create a little cool, you know, platformcompany, if you will, right? Now I'm working for, I'm working for Steve Swan AIcompany over here, as opposed to Steve Swan Biotech, right? I'll work for theseguys. But who's serving SS, AI is servicing Steve Swan Biotech. You know,you're kind of working with the same. And then on top of that,

the tech companies will also, I think, paythem better. Pharmas, they've got their salary bands. So, and it is what it is,you you can't change that. And the tech companies are tech companies. So theycan layer on what they need to layer on to get the right folks. Yeah. Yeah. So thatwas kind of like my next question, which is like, what are some of thestrategies that are more effective in attracting this talent, right? In thiscompetitive market?

Tech companies we know can pay a lot.Healthcare companies, pharma pay very well as well, but not as much as tech. Sohow do they try to attract? I know there's like one angle about talking aboutdoing well for the humanity, right? Like, know, that's one angle. But what aresome of the other strategies that these companies are using? Well, when youtalk about doing well for humanity or whatever the mission is on the company, Ithink you need a person

or a group or whatever you're doing,because getting the right talents, multi-pronged process, right? And one of thesteps in that process is attracting the talent. How do you attract the talent?You gotta tell them an effective story, right? Because you're identifying itfirst. We can all find that, right? We all have the same database on LinkedIn,but then it's qualifying them, then it's attracting them, then it's actuallyclosing it. in order to...

attract and close, you need to be aneffective storyteller and be able to articulate to that person. First,understand what they need, understand what the company needs, bring thosetogether. If the candidate only cares about stock options and it's a big pharmacompany that can't give them, we're already off to the wrong start, right? Butif they want a small startup where they get a bunch of stock options of acompany that potentially could grow, now we have to explain to them, okay, thisis why these options could make some money and this is what they're goingafter,

and then they continue to investigatethrough their process. That's some of the things that they're doing. And toyour point, the mission within pharma is something that does attract somefolks, but not everybody goes towards that, only some of you. I always make theanalogy of looking for a new position as to cars, right? So there's a reasonwhy cars come in all different shapes, colors, options.

Positions are the same thing. Do I onlycare about a three mile commute? That's a different story than if I care aboutthe bigger mission. It's a different story as to whether I am at a smallstartup that gives me a lot of stock options or whatever. What they're doing toattract more talent is to try and figure out, they gotta figure out what thetalent wants and what it needs. And how do you see this talent especiallycoming from other industries to

be settling into pharma? Do they do or dothey at some point get frustrated and go back to the tech? Because seeespecially these people we're talking about what data scientists they they doneed to at some point need to understand how the pharma business works which isa pretty complex business - it's not that easy to do that, right? So like areyou seeing any trends around that at this point? Of them going back to...

Yeah, so people coming from differentindustries like tech background joining the pharma industry, then are they thensettling down saying, okay, well, you know, I like this, or are they sayingmore like, I don't know where this is going, let me go back to tech?Personally, I think you can mitigate that from day one, right? The attritionback to tech industry. Unless you're just looking for a hired gun to come inand you don't mind if they leave. But if you really look for somebody to joinSS biotech, right, for the long term,

make sure that you understand what yourdrivers are and what their drivers are, right, from minute one. Otherwise, youknow, that's not gonna work. So let's say, for example, I am a small company,small, and I consider small, you know, a few hundred million, even if I havesome revenue, a few hundred million bucks. What kind of person's gonna do wellin that company? The person that does well does come, probably, would come fromone of those companies, the tech companies, because they know what it's like tobe an entrepreneur. They can take all different inputs from their organization

and make a strategy out of it. If you'reonly used to, I don't know, a Pfizer, a Merck, a J &J, I'm not trying topick on anybody. But if you're used to a bigger organization, I guess I shouldsay, the strategy's already handed to everybody. The direction's already handedto everybody. But if it's a small place, I call it helicopters because you canhelicopter up and down, all the way down to the technical, up to the roadmapand the strategy, then all the way up to the C-suite, right? And you canarticulate all those levels.

And you're good at that, right? And that'swhat you like and that's what you want. Small biotech can give you that. Now,when the small biotech grows, i.e. Regeneron. When it goes from, you know, Istarted working with them when there was 800 folks, now there's 14,000. But itwas a different conversation for me back then. You know, it was very ambiguous.It's a little more clear now, right, Where they're heading and what they'redoing. So it's totally different. If you get the person that does that and hasthat

desire to stay entrepreneurial, they'llhang with you until they get to a billion dollars in sales or something. Butthen they'll either migrate back to go to another biotech, they had a greatexperience. So to answer your question, I think you can tackle that from thebeginning and it all depends on the individual and what their desires are andwhat their needs are. Okay. And as you say, you know, there's the, there's thedemand has grown quite a bit. There is like a shortage of talent.

What are companies doing to address that?Are they coming up with some different ways or creative ways to address theshortage of talent? Or do you think they're just finding the talent thatthey're looking for? I think they're just digging for the talent, really.They're just hunkering down and digging harder. Because you don't want to hiresomebody who's looking it up on Google, right? So they are looking outside theindustry for the junior level folks. They're hoping to, like I said, groom,

you know, bring them in as data wrangler,right? You know, get them out of school, get them as an undergrad, you know,and then offer them a master's because they're going to need a master's to keepgoing in this field, right? In my opinion. You know, or they find a good placewhere they can find folks that have some of the skills from, you know, say aCapital One or whatever, right, that they do a good job with their folks andyou like that kind of skill set.

But you don't always find that, you know.But if you can find one, stick with it until it doesn't work anymore. OK. Andis there like, you talked about one strategy for it, to get the talent in AI tolook outside of the pharma industry. Are there any other different type ofapproaches that you're seeing for recruitment by the pharma companies to getthe talent in data and AI? No. I mean, they're not.

Yeah, again, they're getting the juniorfolks. They can try and get them from each other. They can try and get themfrom other industries as long as the skills are transferable, but they're not makingthem. They're not grooming them. They're not bringing them. They're not takingthe DBA and making them into an AI person or something. Again, it goes all theway back to, does that person want to do that? Because you can't make them dowhat they don't want to do. And they're already going to be -

anybody who's ever thought about AI isalready in it because they can't get away from it, right? That's true. That'strue. So until the retraining, right? Like retraining your own deal, right?Because it's always like, you know, hard to get talent from outside. Are theyretraining people more or you're not seeing a whole lot of the retraining? I'mnot seeing much of that at all. Okay. Because the kids are coming out of schooland they have it. Now the kids that are coming out of school,

you know, even with one two three yearsexperience, they're making a decent amount of money  as a data scientist or whatever and they canmove in all these different areas.  yeah,yeah, okay, okay. So what advice would you offer to the biopharma companiesabout how they can ensure they have access to the talent that they need in thisarea? I think, like I said, I think what they have to do is they've got tothink a little more about grooming them and bringing them up - the kids

that can actually come into these roles andthen continue to move through the organization. They're gonna be grateful forthe opportunity, right? And they'll be able to branch out into, they don't knowif they're like clinical or commercial or whatever, you know? If a company, Imean, imagine this, a company had, you know how they have, what do they callit? The training programs where they move people around, the executive trainingprograms? Imagine if they had an AI training program like that, how cool wouldthat be?

I mean, these kids would be all over that.Some of them would, right? Some of the computer science kids and such, I thinkthat'd be awesome. I don't see it. I don't see it happening yet. I mean, itcould, right? But I don't know. I just thought of that here and now. Butthere's different things that they could do. Again, the talent is not a lot ofit is there for them. OK. OK. And looking at the other side, what advice wouldyou give to AI and data analytics job seekers

to get ahead in the biopharma, to go to labthe right roles and move ahead? So I have a kid that's in data science and I'vetried to, if she's not into biotech, she's not into biotech, boy, they could,the biotech industry could use any one of them that wants to come into the industry,right? So my advice to anybody in data science and such would be to at leasttake a look at the biotech industry, or pharma, you know.

there is a need for both on the commercialside and on the, you know, R & D side. Right. So there's a big need there.It seems like insurance is always kind of doing it. The insurance companies,but that seems like it's more of a linear analysis because the data sets arenot as, diverse, right, as they are in biotech, you know, whether it becommercial or whether it be R & D, but my advice would be to anybody that'sstudying in the field, at least take a look at this,

at this industry because there's a lot ofdifferent avenues and it's a good place where your work-life balance stays incheck as opposed to a lot of the others. Yes. And I know we can't reallypredict the future here and right now everything is shaken up with GenAI, butif you had the crystal ball, how would you see the hiring landscape for AI anddata analytics evolving in biopharma

over the next few years? Is there anypredictions that you have? I think there's more kids, like I said, that arecoming into the field, right? Well, let me start with, I just posted somethingtoday where IT, just IT overall, technology overall, the unemployment rate inAugust was 6 % in IT and in September it went down to 3.8%. So companies arepicking up IT professionals, right?

That's obviously AI included and it's thatdatabase person we talked about, that ERP person, so that's everybody. But I dothink that the trend is going to continue because of the way that AI,generative AI, data analytics is going to add to the bottom line, right? Theleaders are going to see how much smarter and faster their organization canpivot and work if they have just some of this in house. I mean, you've seen it,you've seen -

You point directly to the ROI on thisstuff. I know you have to, right? So, it's, it's, it's great, you know, and,once they see it, they're like, wow, this is good stuff. So there is going tobe a lot more demand, but it's, it's getting pushed into everything, right?It's getting pushed into, like we said, sales and we already said, you know, wealready said commercial and R & D, right? But I mean, your supply chain,your ERP, your human resources. I mean, it's every, I went to a conference withHR and AI. it was great,

you know, it was great the way that theywere going through it all, you know? Okay. Now with AI coming in, do you seesome like basic IT jobs getting lost because now AI can actually automate that?I don't think so, Amar. I think what's going to happen with these AI and GenAIroles, this is just going to make us all better, faster, smarter, right? So Ithink it's going to enhance what we do, but what it's going to allow us to do,you know, you and I, it's going to allow us to focus on higher level tasks,right?

Some of the lower level stuff, maybe we'redoing comparisons, I don't know, we're coming up with data trends or whatever,the AI and the GenAI can do that for us, or for writing something, right? GenAIor  chat GPT can develop something for usand we can rewrite it or redo it, but it's just gonna, in my opinion, free usup and make us better, smarter, faster workers. I've even read, one of the CIOsthat I did one of my podcasts with, he posted something about how he feels

that AI is going to make everybody betterleaders and better managers by being able to be more in touch with his peopleand what's going on and those kinds of things, which I thought was a differentspin, but I think it's great. So I don't think it's going to replace. I thinkit's going to enhance. Let's hope so. And thank you very much. Well, SteveSwan, CEO of the Swan Group, thanks for your time today. Amar, thank you verymuch for having me. This was great.

Amar, talents and issues we started totouch on in recent weeks, but haven't taken a deep dive on. What did you think?Yeah, so it's pretty interesting, of course, as we had imagined, right, thatwith the booming of AI and so there is a lot of demand that's for talent. was interestingsome of the insights that Steve provided about where

the companies are looking for people fromthe industry, more for the experienced leadership roles, for some of thesestarting roles, they're looking outside the industry and also talking about theneed for data, right? So yes, having data is great, but it cannot be therewithout the data. So he's talking about need and the companies realizing theneed for

people who can do the data wrangling andgetting the data in shape for the AI. Well, data and what we're able to do withit is reshaping the life sciences. Steve talked about the clinical side leadingthe demand because that's where there's been the greatest ability to put datato work. But he also called research the holy grail. How do you see thatshifting demand for talent?

I mean, as you mentioned, we are seeingdemand across the board. With research, I know there's a lot of the smallcompanies as well, right? A lot of the small biotech companies, we have hadCEOs of many of those appear on our podcasts here who are working on somespecific data science problems that will help in really creating novel drugs.So I would say that's going to continue. I know some of these

pharma companies are also looking at that.I think like I mean, the demand has been there, right? And the demand will, Ibelieve the demand will be there. I mean, what I'm just afraid of is that rightnow there is the hype. Once the hype goes down, what's gonna happen, right? Isthe demand gonna go down? Are people gonna over hire, right? That's somethingthat I'm kind of like thinking about. But I would say like, yes, of course, inresearch, yes, we are looking for some game changing things. There will be highdemand in research. You asked about the openness to

bringing in talent from outside of theindustry versus life science specific talent. The answer depended on what thespecific need was, the level being sought, leadership first, frontline workers.Do you think the industry needs to be doing more in terms of training and outreachfor a new generation of hybrid workers? I absolutely think so. I think that'sone of the gaps that the companies have. I see a lot of these companies, someof these companies have figured out what they want to do.

Some of these companies haven't even figuredout what they want to do. They just want to be in this field right now becauseof FOMO, right? They don't want to miss out. But in terms of how they are goingto groom this young talent, how are they going to have the career paths forthese young people? I don't think a lot of companies have really thoughtthrough that. They're just figuring out. They're trying to get the people andfocusing on the problems that they have.

What does that mean for the new talent? Idon't think, there needs to be a lot of work that these companies need to do. Ican tell you by talking to a lot of the talent in this area, when they go to atech company, they know their career. They work for Google, they work for evena startup tech company. Two years down the road, yes, whatever they have donenow, that experience has relevance in their next job.

But let's say they do something, work on aspecific problem in pharma for two years, and they're seeing that the pharmacompany is not really showing them a career path, and they want to go back totech. Well, the question's got to be, well, okay, well, what they did last twoyears, how is that relevant for tech, right? Because a lot of that is going tobe specific to a pharma business question, rather than developing necessarilythe new tech. So that's a question that they need to answer. And the pharmacompanies do need to think about

that point of view a lot. Well, when youwere talking about recruitment strategies and the challenges the industry face,sorry, I'm going to start that over. When you asked about recruitmentstrategies and the challenges the industry face, you also talked about whetherthe industry will, the mission of the industry will be enough to offset thecompensation issues to compete with more traditional tech, which may provide abigger upside with stock options.

What do you think the challenge for theindustry will be to get the AI and data talent it needs? It's going to be thatwhy would they come and work for a pharma company, right? And there's, yes, ofcourse, there's a mission. I've in fact talked to a few people who were techpeople who, especially after COVID, they actually wanted to move to the pharmaindustry because they thought that, with the pharma companies,

it's actually doing good for the humanity.And then there will be something, they will get more than just thecompensation. So they were interested in that. So yes, yeah, so we have thatgoing for us, but you have to give a good experience to these people. You haveto have them, the rest of the things about like how, in addition tocompensation, how their engagement is going to be.

Are they going to learn about the industry,right? What is going to be the plan for them to understand the business more?Those need to be there. So yeah, so that's what the companies do need to domore. I do think that mission is still a powerful driver for a lot of peoplethis industry attracts. If you want to hear more from Steve Swan, check out theBiotech Bytes podcast. That's Biotech Bytes.

Another interesting conversation, Amar.Until next time. Yes. Thank you, Danny.

Thanks again to our sponsor, AgilisiumLabs. Life Sciences DNA is a bi-monthly podcast produced by the Levine MediaGroup with production support from Fullview Media. Be sure to follow us on yourpreferred podcast platform. Music for this podcast is provided courtesy of theJonah Levine Collective. We'd love to hear from you. Pop us a note at danny atlevinemediagroup.com.

For Life Sciences DNA and Dr. Amar Drawid,I'm Daniel Levine. Thanks for joining us.

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