Welcome to the first episode of The Effective Data Scientist podcast. In this episode, we go over why this podcast is the right one for you, how this podcast can help you, and what it means to be an effective data scientist. We also introduce ourselves-where we are from, and our expertise.
Stay tuned while we discuss the following topics:
- Introduction of Alexander and Paolo’s background and expertise
- How both their expertise, both technical and soft skills, can help you to be effective within your organisation
- The topics that will be discussed in the upcoming episodes
Alexander Schacht: This is the first episode of The Effective Data Scientist podcast. And like every first episode of a podcast, you will learn about why this podcast will help you whether it’s the one for you to listen to and you will also learn a little bit about Paolo and myself and where we are coming from and what our expertise is. So stay tuned.
Welcome to the first episode of the Effective Data Scientist and I’m recording here together with Paolo. My name is Alexander Schacht and we are starting this podcast for data scientists with a couple of really, really interesting topics. But first, let’s get to know and introduce ourselves a little bit so Paolo, why don’t you start? What was your career in a couple of minutes?
Paolo: Thanks Alexander. I’m a statistician working in the pharmaceutical industry and I’m based in the middle of Italy in a beautiful region called Umbria. My previous experience is merely gained in academia and the public sector. I was a consultant for several research bodies, public bodies and mainly in healthcare. But, I also had some experience with other kinds of data. And of course it’s really a pleasure to be here and maybe I’ll be trying to share with others what I learned so far in terms of analyzing and hopefully interpreting data.
Alexander: Yeah, and you have decades of experience. Yes.
Paolo: Maybe it’s something to say to our listeners that I’m 44 years old, so it’s approximately 20 years of experience in statistics or data science.
Alexander: Yeah and there’s a couple of similarities for me as well. Starting to work on statistics and data science. In the mid 90s, I was studying mathematics so coming from that background and of course adding skills, in terms of coding, it’s time I had my first course in C++. And of course, and obviously a couple of different coding languages came on top of that. I did my PhD in statistics and then also joined the healthcare industry and have worked there quite a lot as a statistician. I Analyzed all kind of different data but of course, with emphasis on medical data and there’s two things that I have a big passion about, one is statistics and the other one is helping people to become better in terms of their influencing skills, in terms of the their leadership skills, and that is about lots of different things. It’s about, you know, becoming better in terms of communication, becoming better in terms of building teams, networking and building relationships, Influencing others, understanding where others are coming from, understanding how things fit into the bigger picture and how we can drive forward innovative ideas and changes and very often that it’s really, really important for data scientists because, we want to have a better approach for something or we want to inform a decision and be convinced that data-driven decisions are a much better way to make decisions that only rely on gut feelings. And so I think there’s a big role for technical people like us that we can step up.
We have a lot of strengths here, but as a community we also have a lot of limitations there. In terms of one thing, for example, we are rather detailed people, we love all details, we love to go to speak to these, we love technical skills and we excel on these and that comes with a price on the other side. So that you know, if we talk to more senior people, senior managers and senior supervisors. They are often not that interested in the details and mostly, they are completely not interested in how we came up with the answer. So, working on this gives me a lot of satisfaction and this is actually the second podcast that I have started. The other podcast is called the Effective Statistician and yeah. Paolo and I have actually met over this podcast a couple of years ago.
Paolo: Yeah, Alexander, if I may talk about the effective statistician podcast. I’m quite addicted to podcasts in general. I like to list and I don’t know, maybe ten podcasts, so I’m looking forward to the new Episodes for this podcast. And in the end, the effective statistician became my favorite one. And I agree with Alexander. I like the Effective Statistician podcast because there is this mixture of soft skills and methodological skills covered by different episodes by different people. And I agree with him that we need to be better as data scientists in terms of communication, but also learning more in terms of productivity habits or working as a team. Because, mainly, I also did a Ph.D program in mathematics and statistics and it was a very exciting experience. You know, all day working, studying, trying different stuff, but many of us are used to working on an island. So, maybe I would like to be isolated from the rest of the world and be so focused on this problem solving approach and we need to spend more in, you know, going also in this communication skills, teamwork, different workflows, you know, Agile, versus support structure, that workflow stuff like that. I think that we will have a lot of fun discussing both methodological and these productivity communication things.
Alexander: Yeah. That’s a very good summary. I think we’ll spend a lot of time talking about statistical things, especially and we will really start with, you know, all kinds of different basic concepts of Statistics. So if you are already a very, very experienced person and you can see that as a, maybe as a recap or something, just to rethink about it, because that’s also very, very helpful or maybe you see some kind of nuggets in there, if you haven’t been aware about so that will also be something helpful if you’re very new to statistics it will be a really nice complementary experience to things like videos and books because this podcast you can listen to while you’re running or while you’re driving or while you’re cleaning the house, all these kinds of different things.
We’ll also talk a lot about, you know, leadership and influencing skills, like Paolo just mentioned and and finally probably he also says, some kind of productivity tips because this is also something that I think is really important, if you can maximize the values that you get from your time, then you are less likely to be all overworked and focused on your efforts is a big thing in achieving more with less time. Yes, it is. This is what the podcast will be about and will be published on a weekly basis. And if you want to find out more about this podcast and head over to the effective data scientist.com. So once again, the effective data scientist.com and there you will find the show notes additional material from us. You can learn about our background, you can see a lot of pictures and all kinds of other things.
And if you like this podcast, then share it with others because there are so many data scientists out there and this is all for free here. You don’t need to, you know, pay anything and to just share it if you find it useful.
About the presenters:
I received my PhD in biostatistics from the University of Göttingen in Germany. I have authored more than 70 scientific manuscripts in peer-reviewed journals and regularly speak at international conferences – both statistical ones like Statisticians in the Pharmaceutical Industry (PSI) and medical ones like the European Dermatology Congress (EADV).
In my 16 years of experience in the pharmaceutical industry, I focused on late-phase work. Since a couple of years, I head up a team of highly capable statisticians at a major pharmaceutical company. In addition, I serve on the board of directors of Statisticians in the Pharmaceutical Industry (PSI) as Chairman of the Communications Committee.
Statistician and statistical programmer, Paolo received his PhD in Mathematics and Statistics from the University of Perugia, Italy. His work investigated several applications in the fields of graphical modeling and latent variables.
For 20 years, Paolo has worked as a statistician in academia, the pharmaceutical industry, and the public health sector. He has also led and co-led led several research projects funded by the Italian Ministry of Health and the European Commission. His main research interests are bayesian modeling, evidence synthesis, patient-reported outcomes, subgroup identification, and data visualization. He is an active member of the Statisticians in the Pharmaceutical Industry (PSI).
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