Jean-Vincent Le Bé @ Nestlé | Data Science Hangout
- December 9, 2023
- Posted by: MainInstructor
- Category: Assembly Data Science Entrepreneurship Go Python SQL
Video Title: Jean-Vincent Le Bé @ Nestlé | Data Science Hangout
Welcome to the Data Science Hang on everyone. The first one since PositCOMF, for all those that I met. It was great meeting you. This is your first data science hangout. Welcome if this is your one hundredth or more. Welcome. We’re happy to have you. I’m filling in for Rachel Debsey today.
She’s ordinarily the the host. She’s taking a well deserved vacation. So I’ll I’ll do my best. This is a casual open environment for data scientists, data science leaders interns, students, what have you? So we want it to be a free flow in conversation,
Super welcoming. So, you know, use your best judgment Be nice. There’s lots of different ways to participate. You can, chat in. Hannah will share a slide or link where you can ask anonymous questions, you can raise your hand, you can turn your video on, you can, to sort of speak,
So it’s super casual, whatever works for you. I’m really happy to introduce our leader today, Jean Bison, from Nestle. So I’ll hand it over to him to do it to a do an intro and we’ll get started. Okay. Great. Thank you very much,
For the the nice intro. And, yeah, just to to to build on, what you’ve just said, I’ve been assisting to, I would say some of these, these, hangouts, honestly, it’s it’s also sometimes collapsing, but it’s in the end of the day here because I’m in Europe.
So sometimes it’s a bit difficult to attend, but I just want to reiterate that, it’s a really, it’s a pleasure for us to be here and to Anor. Anor, to have been asked you to share my my experience with you. And, and just, I’m ensuring then this experience as a person.
So, just to make sure that, everyone is also aligned on this. I’m not here representing Nestle as a Nestle employee. I would say, but I’m happy to share with with my experience throughout my, different companies also and the, and more recently in this day, of course.
So speaking of which I actually, medicated as a physicist, I did, at physics in the Swiss Federal Institute of Technology in Ozan. And then after that, I did a PhD in a neuroscience So, what I have, and I was exploring the connection, between neurons, doing more biophysics, and, that would be through electrophysiology.
That’s where I started to do some, data analytics. Or rather, heavily involved in in, both analyzing the signal to get, some relevant features out of that and then doing statistics to to get the the, yeah, what would be, the, what would be the, the message or learnings to get from that. Basically,
I was exploring how neurons were connecting and disconnecting, over a twelve hour period. With, with actual slices of tissue. Then after that, I moved to the industry, and then I started to work as a process engineer in, Valtanic Technology, which is a company’s license,
With, also a production site in in Solum, in the US, in in Ohio. And, I worked there, so as a process engineering macro, electronics, to do assembly for medical devices. We are working on, outside medical device, but also some, some implants, and that was a bit more challenging because
And that’s where we have this, this neuroscience also that was interesting for for the company, within the neurostimulator. So I’ve been there, successively, process engineer then head of, engineering, and development. And then the head of technology, taking care of the the the the the portfolio of technology and interaction with customers.
So taking Vatronik is a service company. So we don’t have the we didn’t have any direct product, but we would, develop an industrialized products for customers. And then, after seven years in that company, I moved to Nestle as a data scientist. And then I, there, I worked in different projects,
In the system technology centers. So, is located in Auckland, Switzerland, where this, this is the center where we develop all the what? So called system. So system is the conjunction of the machine, dispensing machine, the packaging, which is functional to some extent more or less, but sometimes more than less. And the product,
And one of the most known is Nespresso. I would say, the Nespresso system with the the virtual lines that you had in the in the US or, we also have Dolce Gusto and different things like this that are, around that. So I I worked there, really in different project as the data scientist,
With the land of experiments, with this statistical process control with also some automating, data analysis that we’re coming from the labs where the lab would generate a lot of different files and we would need to have some, apps that would then, and that would be shiny apps then. I know it would be
Using, and analyzing the data to to extract the the knowledge of it, and then drive the drive the development And, recently. So this summer, I moved to necessary research. So moving a bit away from the production I say closer to, to more, upstream, process in a research and development. And now I’m,
Still working in, you know, in in in programming and having these, on, more applied in the food science. And so it’s a broader than just the systems tree touching all the necessary categories there. Aside on, say, part of my activities also understand that could be
Interesting and that’s why I’m sharing it also with you because there might be some questions, also around that. I’ve very early in Nestle joined the network. So we have knowledge networks in Nestle. In r and d where we actually connect, different people from different sites, just to give you an idea,
I don’t know exactly how many sites, but I think we have sites all over, the, the globe. And, then sometimes that I scientists are there one of their kind or maybe two of them. And, it’s good to connect so that we can share on experience.
We can share on on tricks. We can share our own knowledge and, and also, get some some coaching and and, and entering also, each other. So this data in its network, I’ve joined when I joined it almost at the same time. And, more recently, I’ve been in,
Or for a week for some time already in the leadership team. And for the past two years, I’ve been needing that network. It’s already coordinating the the different activities for the net this network in the, in earnestly. So, so what that’s a bit, about my history.
In a in a in a short term. I don’t know. I haven’t looked at the, at the questions. I don’t know if you could what some of the questions are we if there is any question on this? Questions questions typically come in, you know,
Maybe starting now. So if you have any questions, you know, please type into the chat or, you can use the slide or you can just, hop on, with video and audio. I see Mike actually, has a question, Mike. Nice to see you. Yeah. Hi there. John. This one.
So what is the size of the data science group at Nestle? And I’m sorry if I missed this. Well, I didn’t mention. So that’s a good question. I would say that, it depends how you how you define data typically, the network,
So I will start from my closed community or my closer to me. The the network, I mean, we are typically around fifty. That would be for R and D, fifty sixty, but not everyone with center completely exhaustive. And they more recently did, data science. So,
And and these people I would say would be more really in the in the development and kind of hard coders. I would say also on people either using r or or a or Python or both. But then when you extend also to a lot of analysts,
That would then be also using some more less coding or no code solutions. It becomes bigger. And when you look at also beyond R and D, would say a number that came recently up is something like around a thousand. A thousand knowing that Nestle is three hundred thousand
People around the world, including all the factories, of course, and then you can have a very diverse, approaches because we have people in the in the business who would be, more looking at you know, dashboards, I would say it’s really around the data visualization, which is already, teaching a lot. I mean,
Basically having a just showing what is there. I mean, how many times I’ve got people coming to me with an Excel sheet and say, oh, we see this and this and this on the numbers and I say, yeah, well, try plotting your data. And that’s one of the thing we say,
Also in the in the in the the the course we give is is really plot the data the first thing to do. So this is already very valuable and, here, typically using for our BI, type of solutions. Also, we have many people in the business doing this.
There is also a lot of activity in the supply chain, that I’ve, held off for for the demand planning and this kind of thing. So there are there have been things like this. And, any in various fields of nestle. So that’s why when you extended beyond r and d, it goes,
It goes there, but it might be in also approaches and, and techniques that I’m less familiar with, as I’m much more into, I would say, technically generated data or, yeah, machine or instruments generated data. Yeah. If I may, can I have a follow-up question then, Rob? So,
When you were talking about the communities to try to bring together, you know, data scientists or practitioners then. So is that primarily your fifty or is that actually reaching out broader? Now it’s primarily the fifty, I would say, because it’s it’s really much more around, doing the same activity. Well,
We’ve seen here in the in the hangout so we have some commonalities across what the different activities of the gas centers, but, we also have, might be quite different. And I think that when you are treating some some, I don’t know, data for supply chain or human resource data,
Or data coming from a mass spectrometer. It’s not exactly the same. So you have somehow the same techniques behind, but, also where we are more than just machine or calculating machines. These people also, we are having some knowledge about the domain. And and typically, when I was, you know, you know, I know,
Now I think a lot about coffee also because I worked for coffee projects. And, when I, I was there, you know, it’s releasing the connecting the dollars for sometimes because you’re like, people, they come with a request and they have a certain perspective from their project. And then also as a data scientist,
You can say, well, look, I I understand what you what you would like to do, and I can understand because I also don’t know what is the context and what is the science behind, to some extent. I’m not pretending I’m as a direct level, but to some
Extent, we know this this, and then we can, we can help. So that’s why this community, I would say more in in r and d, which is already pretty challenge to manage. Because we see that we have a different, different, also approaches
Have to know that in in Nestle R and D, we have, so food science, as you know, the most obvious our systems then the product technology center. So they’ve got the product that you wish to find on the shelves. We also have then Karina, which is part of Nestle,
And you may well know. And, but we will set health science. And typically in the Institute of Health Science that would be more things around, you know, more nutrients. So it’s around intake, food intake, but it’s also, regarding different, type of product would be delivered in the hospitals, for instance, and that would,
Then require different approaches and clinical studies and these kind of things. So it’s already pretty diverse and that would have been that is still, I would say a challenge to have sessions where everyone is interested in in the topic. So, sufficiently, I would say sufficiently, we said that in English, sharp in technically,
But sufficiently broad so that we are not lost by the by the science behind if it’s not in our, more familiar domain. So, so yeah. Nestle is a massive company. Right? Like, I’m I am assuming most people are familiar with it, but Jean Vincent,
Do you wanna just sort of maybe give folks a lay of the land of, like, Nestle as an entity? Well, so I, as I’ve just said, to give a number. It’s around three hundred thousand people, around the world. It’s, I don’t know how many hundreds or thousands of factories.
And then, what it has this particularity, as I said, that it’s, it’s kind of so it’s a Swiss base. Swis originally, from so the the headquarters are in Verde, in Switzerland, by Lake Geneva. But she’s called Blacklenmore. This because ladies and stuff has a whole debate on the region. And,
It’s Swiss spirit in the sense that it decentralized even though there is a center in Verdde, but there is always, a a will to have, products that are adapted to the local markets. So there is a some sort of autonomy that is also given to the different market.
So what we call a market is not necessarily a country but if it’s a big country, it’s just a country, but can be also a group of of some of some countries. And basically, well, it’s I think the first food and beverage, company and our main products and our, around
Coffee, we are we are producing a lot of, of Nescafe. Typically, it’s one of the one hundred you mean also of of Nespresso, as I mentioned, so Purina, in the in the pet food. There is also all the chocolate chocolate k e. I don’t know if, if, how, how there is,
How it’s spread. But there are also very, there are products that are typical to certain regions typically. I think Milo is South American. So then there are many, sometimes many brands that I’m I’m even still discovering that I didn’t know existed. So so it’s it’s pretty broad. So but, really,
Supplying a food beverage, around the world, I would say. Yep. And your your previous company, Veltronik, is that is that the way we make? No. It’s how how different is Nestle from from that company? Well, you had different aspects. So first, Verconic is five hundred people, around the world. So it’s,
Smaller than one center in this way. Let’s say they are in necessary search where more than seven hundred are located in in Los Angeles around the Los area. And, so in that sense, it’s, it is, is it was much smaller. So, of course, you can, have also responsibility at a global scale
Much quicker, I would say, in a career there. But still it was international in the sense that Vatronique has to quarters are in in in Switzerland. There is, well, there was at the time a, engineering bureau in Romania, and then there was factory in Morocco,
A CF factory in Morocco in the US, installed on the OIO. And, So there is still also this diversity because in the sense that, so the Marrocon company, plant was ready to to produce, with a, a less expensive, workforce. So it was more a decentralizing
But the US company was a lot more autonomous and serving more the US market. So there was also there also this, aspect of, offering, this, serving their local needs, I would say, from the company. Then it’s different also in, what the activities also is a little bit different because we do not
Do not develop electronic devices in this way, whereas a a battery equals re owned electronic devices. And, yeah, so I would say, there there is also maybe the the size of the company really makes a big difference in the sense that well, there are two things. First,
There there might be some more in Vantronik because it’s medical. So aside from the medical part of Nestle, but, and still, Nestle is a food company. So there is a lot of constraints for around safety for, for instance, adjusting. But, on the other hand,
Initially being a really big company needs to have, you know, all of these process that are inherent to every big company, I would say. And Vartranik was maybe a a little bit more, on we can do different things. But yeah, it’s still to to to a certain extent. So then, I would say
It would be moderately closer to a startup even though while I’m saying this, I’m also thinking that some parts of Nestle are also shaped to be, like, I would say start up, like, or have freedom. So there is still even though there is it’s a big company resort or the process and everything,
Which is giving also a certain, you know, security and coherence, then there is also this this innovation, and, entrepreneurship that is visible into some some areas of the command. Yeah. There is a an anonymous question asking, what is the difference between your role as head of technology and now a data scientist?
I’m doing more hands on things. That’s a data scientist. That was also one of the goal. You know, when you are head of technology, it’s, the activity was a lot around supervising, people, not supervising in terms of people management, but more in terms of, technical outcome that they were producing.
And there was also a lot of activity around, interaction with the the the customers or with the the prospects. So I would then work with the with the sales people. And then, you know, when they they would come to me, oh, I have this new guy who’d like to do this,
And this is do you think it’s feasible? I don’t know if you’ve if you’re familiar with the, with the the the the YouTube video where it says I’m an expert. I can do everything, you know, with the seven perpendicular lines, with blue ink, that are red. You know,
For those who are doing a belt and then you would see these are the types of times of, you know, you’re the the head of technology, the the expert coming in and and then firsthand, say whether it’s possible or not. So, and in that regard, I’m not doing this anymore, at all,
As a data scientist in this industry because I I’m, I’m involved in the project. I would say really much more hands on. And I might then, that there is no interaction with, I would say customer who would have a space project. It would be more internal customers and
Internal, project that are being, shaped and, and, and developed. So, yeah, so so, there there is this hands on thing, and I could really much more focus on the API science activity itself and develop also in that field that is that is also taking off a lot with, no more recently. Also,
On on on larger datasets as well. Very good. Thank you. There was a question, posted by I think Neil’s. Sorry if I mispronounced that, but Neil’s, I’ll give you a second if you wanna ask that yourself. Otherwise, I can ask it. Yeah. Sure. Can you hear me? Loud and clear. Thanks. Awesome.
Yeah. So my question was around, I think you mentioned something about collecting some some data files from the lab using, shiny. So I was just wondering you could talk about the process of around identifying shiny as the correct tool to do this. And then whether you had any interaction with the
Stakeholder to get approval for using it and then, as an add on to that, what development challenges did you meet, if any, when using shiny and, enterprise? Thanks. Yeah. Okay. So, basically, why using shiny? It’s because we’re using r. To start with. So, basically,
The the data analysis that I’ve been doing, so I’ve, we have an internal package that the school has stats and that I participated to in developing, some code around, design of experiment analysis. And, so just to say that, we have a rather intense activity
Using our, as as the software for forwarding our analysis. Especially when it comes to, you know, sometimes from the lab, you can have someone more automatic, automatic, or semi automatic, equipment that would generate a lot of data, like protocols, yeah, like robotic, systems and things like this. And, when it comes to that,
You have, a lot of different files or a lot of files, which are not so different. They are the same structure but it’s a lot of them. And, you know, copy pasting it in Excel. I’m trying to do something out of that is, kind of, painful.
And so, very quickly when I come to this situation, I would then start coding in order to do the loading automatic data running, put things together, have an graph with nice colors using g g plots and so on. So I’m a tidy versus fan, by the way. And then,
Then then with with this, you have your code, and then, you get your colleague from the lab or from the project a different thing. Hey. That’s very nice. But now I have new files, and I would like to do it again. And the same analysis,
And the same display. And then and over and over and over. So after some time you say, hey, look, I can just package that into a nice web interface. And then, and then you can do it all on your own. And then if there is anything doing going wrong,
Then you can just let me know, and I will I will also, I will also then update it. Other things that could happen, typically, to give you an example why we’ve been doing this is So while you start the secret that the coffee machine is is
Pushing water through a system and then with water, you have a system pressure, and then so you have some curves of pressure against time or temperature against time is kind of soon. And then we are interesting, to, interested in what is you know, the maximum here, what
Is the plateau here, what is the local minimum, what is the local thing, and what is the variability. So all these features extracted that are, then the directors into that. And maybe we’ll run their own analysis on the features, but you need to extract this feature And a typical shiny app would be,
Something where they could upload the road data and then get back a CSV file where they have the features analyzed. And extracted. So now how did we manage to have this in the company? So I would say a key thing is to have, a good,
A good relationship with IT because the other the guardians of the compliance for IT. It’s really important and and for very good reasons, especially in a company as exposed as, as big companies, I would say any big company. And, what it it it went. So the the first thing that’s I mean,
At some point I had also, you can also have shiny apps that you just like locally deploy in in in in the computer and then you can also have your colleague, having are installed and then you can just, we set it by these sometimes, by the way, to have, kind of,
Batch file to, just start the process, and then we double click on it, and then it would open automatically, orange, shiny, behind, and then they would have the the page, but then it’s sitting on their computer and if we want to update anything, then we need to to go to their computer again.
So there were some discussion also already from from the center where people say, oh, that would be interesting. And then, you know, connecting with IT, you you managed to put it into the sufficiently secured and closed environment, but still available in the intranet.
And then more recently, we moved with Azure and then, then it goes into into Azure, with Azure solutions. And, and, and then that’s also where you can manage the access. You can manage the visibility of the the the virtual machine. So now we severies is is is about a virtual machine. And,
And, yeah, so the the thing is to have our IT counterparts, that are aligned, with with this. So, basically, what we I was looking at was I could describe the needs that we had as as a programmers and data scientists And then they would, they they came back with some proposal,
And then we moved together to to have something set up and running. So, I don’t know if I covered the the the whole question because you you asked me also, yeah, how how do we, is there any, well, hurdles in the big company? Yeah. We said they choose this,
I’m having the the right the right people, but it’s also a matter of, of, of good compromise, I would say because of course you you are not, in the world as, open source and or you would not, you know, typically our code discretion into a git system, but inside house,
Which would not put our code on GitHub because it’s a private based company. So you need to to have this, this understanding and then you know, also explaining what are the needs that we have TPB and R and D. We have some needs that are, inherent to research and development.
We we cannot predict what we will discover because it’s research and development. I mean, so that and and sometimes this is a bit, you know, difficult to, to handle when you speak with people who are used to, deploy an app and if the app is doing what it’s supposed to be
Doing and we know and we know everything and there is no doubt and that goes with it. So there there was also this discussions surrounding to really understand each other’s needs and perspective because you’re gonna either, go freely, like, say, I’m r and d. I’m creative.
I want to do everything and and and voila, I don’t know. You need to play also by the rules for the good reason and understand them. Regarding your comment about good relationships with IT and Travis, I see you, made a comment. We do hear that A lot,
I like to sort of ask, like, what are some concrete steps or things that you can do to actually build a good relationship with IT because sometimes it can be a statement akin to, like, deliver a great customer experience. It’s like, okay, well, what does that actually mean and look like?
I would say, yeah, the the good relationship with ITs, I would say like any human relationship is, understanding also the the other’s perspective. It’s it’s really, you know, most of the time, really in a very vast majority, when someone is is bothering us, with some constraints.
It’s not by pleasure or just to make our life worse. It’s because they they need to deliver on something. I mean, they are also accountable for something. And then, we need to, to, to make sure that that will also speak the same same or not same language,
But at least we have the common ground of an understanding. So I would say that would be one of the key things. I mean, I remember, like, for instance, yeah, there might be things that you are not aware of, typically on on some of the data access or some of, of the
The the systems access, you say, you know, I would like to have it open, you know, in the the the words of sharing, sharing storage, sharing settings, sharing everything. And then they say, yeah, but to see, this and this got happened and actually did already happen and you didn’t know that.
So listen to the stories that they have to tell us where it comes from all these, this frame that was put in place. And and afterwards, it’s even better because once once it’s it’s well set, then you can really work in a in a confident manner in in a in a safe environment.
So, yeah, I would say understand and listen to to the other, but also, be able to explain your your perspective and your needs, in in a in a, In a most objective way, I would say, but online needs to, to say,
To say, yeah, I need to. This is how I, I see him. And then I see someone is commenting one good relationship with the IT. Do not lose patient. Yes. Absolutely. I think patience is also a good thing, but, but yes, I’m not saying I’m succeeding every time.
Sometimes I’ve made, I’m, I’m, you know, we’re passionate. I would say, but, yes, it’s, try to to understand also their perspective. They’re, I wanna go to the anonymous questions. There’s a few of them. One is do you miss supervising people? Do you still supervise people?
I find that it seems like an inevitable path to progress, but I don’t know if I’d be any good. That’s a good question because that’s exactly one of the reason I moved also to nestle. So I think it’s inevitable to do some sort of supervision. Now, the the great thing as, as,
Have the chance to have in in a sense that we have, a path where we can develop expertise. And that’s what I’ve chosen. So I’m not developing into managing more and more people, having a group than a department, than an institute or whatever. I’m I’m more developing the expertise in data science.
But still with a certain level of seniority, you people come to you and ask you for help ask you for, feedback ask you for having some, you know, can you have a look at what I’ve done and then tell me, and then also people that ask you, well, from your
Perspective, what strategic decision would you recommend So, you know, at some point, you take this also, span, when when when when you were growing, in the career. So, that’s why I say it’s inevitable to to lose some sort of management. So I would say I have I more have,
I don’t have direct people as of now. But I have, you know, indirect or dotted line type of of relationship also. I may supervise sometimes some some students or or in in in case like this. But it’s it’s more, yeah, something. I wouldn’t say like, coaching and and type of activity,
Still with the reconciliation that goes with it because I’ve got experience sometimes that, I’ve got some feedback where, yeah, senior leader would ask some project manager to who’s presenting the project, these are the computers and then, oh, did you check it with Jogasaw and then if they say yes,
Then if they say, okay. That’s fine. So you kind of the the the validation of the expertise, and he goes with the responsibility because if it fails behind, then that’s somehow also your credibility that is at stake. So, I would say that, yeah, that’s really something that,
That is an offering I was in a unique area. There was an anonymous question. I think it would refer to a previous conversation. It’s what is a head of technology? I guess the implied question is like, well, what does that entail?
But while that is exactly in this line of not having a typical direct reports, at the time, I will just, transfer so into the different, groups and, so I would be the go to person if there is a technological question, in in the company. And then, also,
Trying to coordinate across the different groups or different different activities. So that would mean you know, reviewing what the different engineers are doing, giving them also some some feedback on on on the on on the results that are sustained and maybe also some guidance how they would do the next
Experiment or how they would uh-uh address the next, process, challenge. And then he also being the consignor, for a relationship with outside. So when someone is coming and seeing Okay. I would like to do this new implant with that technology. Do you think you can bring me a solution with this? And then,
With having discussed result and having an overview of all the project technology speaking, in in the company that you can have a good, insight and think, well, we think we can do that this in that way, in that way, or, with this and this, challenges or in that time frame and so on.
So that would be also the type of activities. There was a question, and sorry if I’m pronouncing your name wrong, from Hancock. I don’t know if you want to join in. I see you’re on see you’re on video if you wanna join in, but I’m happy to ask it as well. Hi.
Hi. Hi. Yeah. Jordan, thanks for the insightful things that you have been telling. It’s really helpful. One question was more around, shiny application that you are using. What kind of database or data store or data warehouse? So would you typically suggest, as a back end for shiny application.
And, majorly, when there is a huge scale, we are talking about, like, hundred, two hundred GB data, in the back end. Well, that’s a question maybe for some of the positive control channels so that would give you a, a more complete answer. To be really transparent.
I I’m not dealing with that big databases. But from what I know that, that that we have been using, then I would say our chosen solution is around Azure I would say a cloud based solution is is something that, that would, that would be I would say the preferred solution because you
Will not download two hundred gigabytes on your on your laptop. It’s not that won’t work, and you need to to to scale up your your computing power. Which is, which is, I think something that is really a big advantage of of cloud solutions in general. They have this flexibility of computing power.
And then, yeah, I’m not sure how much for shiny apps you can handle size of databases, but definitely what you can do also is have some back end pre processing because depending on what you want to display. I mean, in the end,
You may not want to display the the the the hundreds of millions of points. You want to display some summary data, some some knowledge extracted from it. So I would we then from what I know, I mean, this, I wouldn’t maybe use things like, you know, data bricks or,
Or all data factory in Azure. I don’t know about, about Amazon and and Google Cloud. So that’s why I’m speaking about this one. So I’m not, you know, sending anything. It’s just that I’m speaking about the experience. And then you can prepare your data and then your shiny app can then,
Get the the the the relevant data that is, that is, then useful for for this plane. So and it depends also on on the application because while I’m saying this, maybe you want to recalculate some things based on the full data sets, depending on what is the user,
What is the user input, but I would really, try to, to take care of having as much as possible, pre processing data with things that where you can call, very, very, powerful clusters I I don’t know if you if you had mentioned a situation where
You have some data and then maybe once a day or once every twenty four hours or twice a day, you would have You would turn on the very powerful cluster. It is a lot expensive. Prepare your data, save it somewhere in the data lake or in, structured database,
Shiny works well with, I’ve done some shiny application with, with, with, with, with, Yeah, Azure data lakes. So it’s really like a file system. But you can also work with the with SQL detectors very very well that are in the in the cloud. There is also on,
You can work with with cosmos DB that I’m a bit more flexible. But then, are we imagine something like what you have your big storage then your data bricks would, in r and Python, you can do both. You can, then would query and then get your data, analyze,
Save it in a way that is prepared for the shiny app so that the shiny is a lot more reactive when it has really the minimum data it needs to to be efficient and to serve the the needs. So it really depends on on what you need.
Because if you want to program or to get to recompute everything every time you have a query or a reactive action that is done on your app, they need to be very likely crashed, quite quickly. So so I would yeah prepare this in that way. Understood. Thanks thanks a lot, John. Ahmad,
I see your hand is raised. Yeah. I had a quick question. A little bit about my background is beforehand, I used to, have no coding experience whatsoever. Had to learn as a means to an end. And so I’ve just had a data cam subscription and was
Frantically searching on Stack Overflow before my deadlines and, things experience that I had was a little bit stressful and to be quite frank, I was a little intimidated, sort of, stepping into this newer space, a more technical space. And so, Jean Vas, I’d love to get your perspective on if you’ve had
Any experience with coaching and helping other people lower the barrier to entry in terms of, learning how to code, learning how to apply code coding principles so their data science work because the initial, barrier can be a little, a little daunting, a little scary at at least in my experience.
So I’d just love to hear your experience about playing that mentor role. And if you have any tips and tricks for us, Yeah. Sure. So, first, I would say, the the key thing is that the person starting should be, well, think if you are interested into the into
Coding, then that’s that’s a very good first step. People, there is the first barrier is people, some people are scared by the code and and some are not. So if you are scared, I would recommend you do something else, but if you’re not, then it’s a good it’s a good thing.
And then I would say, Yeah. Maybe two, three tricks, but maybe they’re the kind of obvious. You can, have some example code. Typically, you never you rarely start from nowhere. So you always have more or less good quality. I mean, I well. So I started also coding in my life.
I didn’t know I wasn’t born coding. So, so, sometimes some of the other people who didn’t give me some some some part of code that was okay and then I could start reading it and understanding it and then, adapting it. So you can have a first thing is that this is your example
Code and then, and then adapt it. So maybe having a colleague that can give you also some things. And then, something that is that is really would say helping is to do not hesitate to have regular sessions like weekly or you just review the code. And,
Even if you have a question, then you can go, yes, Stackflow or, yeah, I’m not sure that that Chad GPD is doing a very good job. I thought some hallucinations. So double check with the internet as well. Then it can help, and and then you can you can, but I have,
I mean, I’ve stuck with the flow of the load. Of course. And then, then it’s it’s good to have it’s good to have also this session. Let’s take an hour or two hours. Sometime in the week, We sit together, explain me your code. That’s what are the different intentions. Comment your code.
I would say also don’t hesitate to put comments even for yourself. This is what I’m doing here. This is what I’m doing here, and this is, you know, just a few a few notes, and, and then with someone where experience can then, propose some some
Alternative way of doing the analysis or alternative way of uh-uh or more efficient way. And Yeah. So so in terms of of coaching, it’s that there there is this. Because, yeah, sometimes it happened to me that, you know, I get, I get, to read you a code with someone and then,
You would say, yeah, but you can do it a lot more efficient this way. So it’s not only a matter being, you know, computationally efficient. Sometimes, yes, because it takes several minutes of hours to compute. So you you better have something more efficient that is really dramatically, reducing.
But at some times also, it helps also the code to be clearer. And then if you have a elegant, coding, then In the end, you also have a better understanding of what is happening and and you you keep track and you you avoid hidden mistakes because
In the code, you can have, cases where, the the code doesn’t work and it gives you an error. That’s kind of easy. You will have a capital letter somewhere that it was not there. You don’t have the right package. Okay. That’s kind of quickly fixed.
But I would say the most problematic error is for any does everything, and then it outputs something that is not supposed to output. And if you cannot spot that, that’s that’s pretty complicated. So what does when you have a clear code and an elegant code, you can you can more easily follow.
I would say what is going on, and typically, yeah, that that’s where things like, while we have been, yeah, in entire universe again, almost the pipe, and these kind of things, and we can have something that is a lot easier to to to follow. And,
And sometimes what can be good also is to set some challenges and I set myself at some point. I said, now from now on, my code in R will have zero for loop. I did everything without follow. First is more efficient, and sometimes it helps you also think a bit differently.
But with apply, typically with everything around, applying map, map to the map, whatever you have, the map, the, then, you can do a lot of things and, and then you have this, this, also, way to, to to have something that is working. And I would say,
It also helps developing the functions aspect So it’s it’s also good to say, okay, I can have a function doing this, a function doing this, and you build your, you could learn this way. It’s also in the clarity part. This function is taking my data from this to this.
And then I have a nice team to take into this. It’s the mechanism. Do not agree with me since it’s not more efficient to use. Yeah. Okay. Yeah. There is this parallelization that you can use. But but I well, I I had some examples, that actually was, was, we have more efficiency.
We can we can debate this in a specific thing FPD. There there is one thing that is, a lot a lot efficient, is, however, don’t forget that r is using, in is, I mean, use metrics calculation. So typically, the worst thing to do is to can’t through data frame,
Because, and always think that you can use the object and and, and manipulate them as such. Travis, I see you asked a question, and I’m very curious. Where are you dialing in from? Oh, San Diego, back back home, I guess these days. I don’t I admittedly don’t know a lot about food science,
But I’m so I’m in the pharma side and the notion of whether or not you have to submit data product sir, kind of reports to the FDA or other regulatory agencies, do you have to do so? There’s a lot of conversation in the pharma biotech world
About how to do these things with R. It’s often met with some resistance just because they’re more used to seeing SaaS. I wonder if that’s a thing you’ve had to come across. Oh, well, I haven’t personally come across this. I know some people who are sensitive also software reuse,
In this. They are not personally using it has been used. I’m not in that part of the the the process. So, unfortunately, I cannot give you, any details on this. But they are, there are clearly, a lot of activities around around that anyway, or because we have the safety specialists
And we have even an issued order that is dedicated to for safety. So, so this is really something, the separate and probably they’ve been doing submission also to FDA, especially if we speak about health science, development. But beyond this, I’m planning for that.
I don’t have any experience in in that in that regard for what it is. Yeah. No worries. Thanks. No. It’d be interesting, you know, as as a follow-up sometime, you know, the the conversation, I think, is often dominated by the pharma and biotech world.
But I imagine you all struggle through the same sorts of things, on the food science and health science. I’d be interested if we all chat together, you know. Okay. Yeah. Sure. Let’s burn through a few anonymous questions. How much freedom do you have regarding the tools that you use?
As much as I can justify the need, I would say in a sentence. It’s true. We cannot use anything we want. We need to use approved, tools. But As far as I’m concerned from my day to day work, I
Have, a lot of them. So I have a lot of tools and I have, I would say more than if enough tools to use. Sometimes I come across things that would like to implement. And then, yeah, the process is, we we can come with a proposal,
But then we we first need to come with a with a requirements. We we need we need to do this and this and this. And these two answers my needs because of this and this and this. And then, IT would come back and tell you, well. Maybe you can use this,
Which we already have announced at I wasn’t and sometimes it happened. I was not aware of something and then I I have a new tool I can I can use? That is actually answering the needs. And if really, there is, there is a good reason for, for handling a new tool,
Then then that’s okay. I mean, it can be approved and, and then we can move on to to visit. And I double click and ask what what is the, If you can share the process for a new tool, right, so you discover something, it’s amazing. You you wanna use it.
How do you go about, you know, bringing that into the workplace? Or what advice might you give to others? Well, again, as the coming back to IT security, I would really, or come to, first talk to to IT, representative to say, I would not come saying I have this neutral that is great.
Please die it because that don’t work. I would come say, Hey, you know, I’ve come across this time. In my, in in my work and, do you know anything about, that would help me with this particular challenge. I have that by the way is answered by the two. I found out.
And then in the conversation, you can maybe suggest it and see whether they they know about it or not. Because it’s a lot more, you know, okay, saying, and you can say, I may have a potential solution, but this is really what I need to have as first to be to be,
To be answered in terms of, of of need. And then you can you can propose a tool. I think what’s, how do you navigate the the line or sort of adopting open source tools? Right? A lot of the times, they’re they’re free. So you can just sort of, you know, download them.
Maybe there’s not a purchase necessary. How do you go about, or how do you navigate not over involving IT, but keeping them in the loop? Well, first, some of these free, tools that are already, many many of them are, have been approved because, you
Know, IT kind of also, he is doing the the the job in terms of, scouting and being, aware of what is is going on typically with, we have the examples with with chatty and you know what happened with with Samsung. So, very early, we had some communication internally
Saying, we know there is this coming out. Please be careful about this and this. So technically, we are not, if it’s an open source and free tool, that doesn’t require admin rights to install a new machine. You can do it, but then you are responsible for it.
I think this freedom also always comes with responsibility. So, I think it’s okay. And if you if you address it this way also with, with the with IT who is also responsible for the for the overall security of the of the company. Then then it goes in the right direction because you say,
Look, I would like to use that. It’s always good to have the double check and maybe, ask the question. You can test it on your own personal computer at home. Say, I have done some tests at home, and, it looks good because of this and this,
What do you think flying it into the into this? And if you have this reflection, so you may not need to for edge and every step go to to them. But, if you have behind also, the reflection also, okay, What would be, you know,
Kind of doing a sort of a light risk assessment, also regarding the company and taking into consideration the company’s need and perspective. Then after some time you get used to it and say, okay, maybe this, I’m pretty confident I can do some test and and I’ve used
Some reflection and then you can just double check, and some other things you would ask, from start because, you know, it might not be, so straightforward Yeah. Just make sure that, that you have also this kind of, you know, rational and reflection on all this. Very good.
There is a I wanna make sure we get to all of the anonymous questions. So we’re coming to the top of the hour if you have any burning questions, submit them on Slada or put them in the chat, but, one that we didn’t get to so far is what are your
Aspirations for the future in your role? In your role or longer term? Well, that’s, he’s an HR question. No. It’s really I I think I’m really interested into this whole field of Well, basically trying to get knowledge, you know, extract knowledge and understanding of the phenomenon, from from the from from the data,
And we have more and more data. So I would say, really going in in in in this and being also, aware of what is what is, what is is emerging? I mean, recently, we have if you look at it in in big history, we have been having some some statistics and then,
Doing some linear regression or then it came to run on Verizon. I would have transformer architecture that can be applied beyond texts, in some cases. So, you know, getting more of these things and trying to to connect the dots between the different things that,
That are available or that emerge also from from the academic research, yeah, to we say continue in in that field. So then, by doing this, it it’s, in terms of professional, you know, development, typically in the expert, in the expert, expertise aspect, we can then start to
Also give lectures in in some universities or in some schools and then to see, to also share the your your knowledge here and get the feedback also from from from different from the students and from different people. So I I would say, that would be an achievable evolution.
You know, it’s also to to grow to reach beyond the company. For for this, and which is, by the way, aligned with with the the company. So, company pricing. Very good. So I think that wraps up the, anonymous questions, and I I don’t see any outstanding ones in the
Chat. Obviously, feel free to raise your hands, everyone. I like to typically end or or ask, like, not to put you on the spot, but I guess to put you on the spot. What advice do you have, for data scientists or aspiring data science leaders. Leaders in, in particular.
Yeah, well, You have many different, angles here, but if I had I had to pick one, I would say, it’s evolving, very, very, very fast. So I think, being curious on new things and, always trying to, with moderation, of course, because you need,
Also to to deliver and to be to be efficient. I would say yeah. Vincher, so what what is coming up and and and the new discoveries, and then, don’t hesitate to to to test these new things on, to see if it performs better than the current techniques that you will have so far.
But then also, keep the as a leader, keep the right balance between having new things and still selecting the time for the people that you are maybe leading, to to learn about them and and to still delivering what they have to do in their very dynamic. Because, you know, sometimes
You have to, you have these new things that you want to try, but still the projects are moving on and and good old statistics or mocked classical approach is also delivered well. So there is a good balance to find between between the the house today with what you know already.
That you’re having missed curiosity and openness to to try new things that may also open some new opportunities. Are there any particular avenues or channels that, you use and occupy to stay up to date and and learn new trends? Well, I would say, I I get
Some some news, some news that are coming, from, you know, what do I I’m not registered to a specific journal. I would say it’s more, you know, during, like this. Sometimes also, there is no one channel. I would say really it’s really, therapeutic. I was, as well as the question,
I realized this because, you know, sometimes I get something on LinkedIn and I’m, like, oh, that’s interesting. And then I pull the spread and then see what, what, what’s there. Also, most of the time, we have a specific challenge to solving a project and then,
There is a company or the owner that we can work with. Then that would be the standard would prove, you know, to to better, to better understand it. So I would say it’s three more opportunistic and systematic way, I would say. But maybe that maybe I could change this.
That could be a good, but, so freight was that way. Very good. Well, we are at the top of the hour. So, Jean Minseng, thank you so much for your participation and and leadership today. If folks wanna ask you, more questions or get in contact with you.
What’s the best way to to connect with you? Well, I would say LinkedIn. Linkedin, but, don’t, I mean, just be careful to, send a small note that, it was during the positive talk, pause it, hangout. Because, you know, I tend to, to to not accept LinkedIn
Contacts if I don’t know the person, I haven’t met, you know, kind of a rule I said for myself because, you know, otherwise you get a room. So don’t hesitate to connect but, leave a leave leave a word, and, then, then we can, most probably will come back to you saying, oh,
Let’s have a chat and then, and then we connect. So it’s a good rule. That’s right. But include a note. Very good. It was it was nice seeing everyone. Next week, it’ll be regularly scheduled program with with Rachel, but John Mixon. Thank you so much.
I hope everyone has a great rest of the day and week. Thanks so much. Thank you for watching me. Thank you, everyone. See you all. Yeah. Bye bye.
Video Keywords: Data Science, [vid_tags]
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