Ines is a data analytics professional, experienced in Telco, Direct Marketing, and NGOs.


Ciprian Borodescu: I’m here with Ines – Data Analytics Professional currently at Telekom Romania. I’m super excited, and it’s an honor to have you on this podcast. Thank you so much for being here.

Ines Teaca: Thank you so much, Ciprian, for inviting me. It’s quite an honor because it’s my first time on a podcast, so I’m quite excited.

Ciprian Borodescu: Awesome. Tell us a bit about yourself and how you started your data science career?

Ines Teaca: I would say that most of my experience is in telecommunication and my core focus was somewhere around roles that involve data. So, projects, campaigns, or product development. I would say that I was lucky to land a junior job, like everyone did in university, I answered pre-paid calls. And then, gradually, I moved towards more interesting roles like scheduling and forecasting, insight modeler, and business intelligence Project Manager for a group. I participated – and this is more interesting for the data science part – in a global team that defined Big Data principles applied in a wide organization, and I was actively involved in the data scientist stream. And that was a very interesting multicultural experience.

Ciprian Borodescu: Interesting.

Ines Teaca: Yeah. And besides that, I’ve also worked in a direct marketing agency. It was very good because I sharpened my direct marketing knowledge, like forecasting performance results, targeting, applying control groups, e-newsletters, stuff like that.

Ciprian Borodescu: About marketing, is your opinion that they are really data-driven? Because a lot of marketing agencies say that, but my experience showed me that not a lot of them are actually how they should be. And now with your data scientist hat, you can actually analyze them and be ruthless. Nobody’s listening, so you can say whatever you want.

Ines Teaca: I think it depends, but from my experience, and from the things I’m following on LinkedIn, I would say there are some agencies that are being data-driven.

Ciprian Borodescu: The best ones, yeah.

Ines Teaca: I think it depends on the clients you have, on the market you are actually working on, so it depends. I would say they are data-driven. We are all data-driven in recent intervals. I mean, since the pandemic, for sure, data is part of our life.

Ciprian Borodescu: Our reality, yeah.

Ines Teaca: And because I was saying about my experience, let’s say that, technically, only data science with scripts and programming languages and dashboards and visualization, I would say that in my recent five years, I was doing mostly that – of course, not necessarily being named data scientist per se – depending on the organization or on the team, but delivering data science result.

Ciprian Borodescu: Excellent, excellent. And you’re also organizing R-Ladies Bucharest, and I love how you are going out of your way to promote women and minorities in the R community. What’s the community like and how did you grow over the years?

Ines Teaca: It’s a community about R, about the programming language R. It’s part of a wider story, of an international one, which was started in 2012 by Gabriela de Queiroz. She founded this community with the aim of giving back to the community what she learned through various meet-ups, and somewhere towards 2016, with other very interesting women, such as Erin LeDell, or Hannah Frick and Heather Turner, and there are some others that I might forget, met in a conference and decided to apply for a grant at R Consortium, and they received this grant for expanding the representativity of women in the community. So this is the moment in which, let’s say, for me, it was important because I had access to this community via Slack. And this is how the story actually was brought, even here, through the community I’m leading. So, now the community I would say, is a place for enthusiasts. We are generally people that are passionate about R and data. It’s open and safe. So we are more towards women and minorities in Bucharest. And during this period of COVID, we moved online, so anyone can easily participate via Zoom if they register through Meetup. Of course, they should agree to our code of conduct and, as I usually say, it’s an inclusive meet-up, community. We welcome men, but we are focusing more on women and minorities. I’m really proud because we have managed to give voice to unknown women speakers in Bucharest through these monthly meet-ups. And somehow, I’m happy because, at that moment, I was looking for a location. I spoke to several people, which were all supportive, but I remember the initial prognostic for the future of such meet-ups was something like, “You know, you will meet some interesting ladies, you will meet for a few times and then things will stop.” So, I’m really happy that we finished the full year. So, we had 10 meet-ups.

Ciprian Borodescu: Congrats!

Ines Teaca: Thank you. We have over 150 members per meet-up these days and we are preparing some nice meet-ups in the future. And I’m inviting everyone who’s interested in R. I was actually surprised because during the summer, one of our members and speakers, Maria, actually enrolled in the global program that we are delivering for R-Ladies Global. It’s the curation of the Twitter account that we have, that we are R-Ladies. So, I was really proud to have a Romanian there who moderated for one week the global account.

Ciprian Borodescu: Nice, nice. And right now I’m actually on rladies.org and I can see the map here and there are 67 meet-ups in North America, 42 in South America, 55 in Europe – so, quite a lot of meet-ups and it’s interesting to see that the community is growing also in Romania. So great job with that!

Ines Teaca: Thank you! And this is a dashboard which is done with R, with R. Shiny, just so you know.

Ciprian Borodescu: Of course. Awesome. Okay, so let’s talk a little bit about data science versus data analytics versus machine learning. How would you explain this to an intern or somebody that is pursuing a career as a data engineer?

Ines Teaca: You know, I think these new concepts like data science, machine learning, it’s more a marketing thing.

Ciprian Borodescu: Oh no, you didn’t just say that.

Ines Teaca: I used to decline these types of jobs when I was younger, I think in 2009, 2012, if I recall correctly and there was this quote, which I really like from Hal Varian, which said something like, “The sexiest job which will be in the next decade would be a statistician.” And in the same period, I think Facebook and LinkedIn developed the data role inside the organization. And a new Harvard review article appeared, and it said the sexiest jobs for this century or decade or whatever it’s a data scientist. But for me, just to have it as a data person, I would say that if you are new, you need to be prepared for data – data in all flavors – because you won’t get data on a plate, and then you will take the cookie and deliver a machine learning program. So you need to be prepared to take analytics and set the baseline, you need to apply various methods from data science, all sorts of techniques – now, they developed a lot – and, of course, to use machine learning and put in action various insights or even predictions. So, just somehow, to help an intern or a person who would like to get in this area, I would say that it’s not sufficient, unfortunately, to master only data. I think in such a career, you also need some resilience. Of course, it depends on the organization – if it’s smaller, medium, or larger – you will need an appetite for continuous learning because things in technology change a lot too fast, so you need to be prepared.

Ciprian Borodescu: Absolutely. That’s a good point.

Ines Teaca: Yeah. And you need to have good communication skills, for sure. Back in the days, it was easier just to receive some specifications, you would deliver a report, and then things would be over, even if the report would include forecasting scores or just analytics. But now, it’s also important to have good communication skills and to communicate those resources to generate this insight that everybody is looking for.

Ciprian Borodescu: I think you’re touching on a very important point there. I’ve seen a lot of teams that have cross-industry subject matter experts. I mean, you do have to have maybe a machine learning engineer, a data scientist, but you also have to have somebody that is a subject matter expert. Let’s say if you’re working with IoT, or retail, or Telekom, you absolutely need to have that and, of course, you need to communicate within the team because nobody knows everything, right?

Ines Teaca: Yeah, definitely. Definitely.

Ciprian Borodescu: A hot topic nowadays is digital transformation. And AI is part of that digital transformation, but it’s the next step. At least that’s how I’m seeing it. And I’m wondering, do we need to go through a similar painful experience like this pandemic for companies and people to seriously adopt and embrace AI? Do you see something like that happening? Or is it more of a gradual approach that’s more suited?

Ines Teaca: I don’t know if I am able to give a recipe for such a complex subject, but I’m definitely agreeing with your point of view. I think AI is here to stay. I mean, for sure AI is needed not just in the digital transformation because AI can be used in various use cases. I would say AI is not just chatbots and real-time engines. I think it’s more than that. I don’t know if we have seen at this point all the use cases that can be delivered. For sure, some infrastructures aren’t yet built, so we need to stay here and see how things will evolve. But from a personal perspective, I would say it’s very hard to say which is the best recipe to use for it. I’m surprised, also, by the fact that I read recently that this pandemic actually helped to advance the digital transformation with around two years. So this is very good for us. I mean, I’m working in a digital team and I felt it, and I was somehow happy because we applied this approach that you were suggesting – the gradual approach – and now, with the pandemic, we just have to be fast. So, I wouldn’t like to have a new pandemic, but I would like to advance with AI. I think AI is here to stay, it’s here to help us. I mean, I don’t see AI as something bad or something that would take our jobs; I would see AI as an enabler.

Ciprian Borodescu: That’s a good way to put it. Yeah.

Ines Teaca: Yeah, this is how I see it. We definitely need to have teams and people covering this topic. It’s needed.

Ciprian Borodescu: One of the things that I’m wondering is how can AI startups succeed with limited access to funding and talent? I mean, what are the levers that we can pull to push through and reach a point where AI is no longer considered elitist? Because the reality today is that AI is part of the AI tech giants like Facebook, Amazon, Apple, Netflix, Google. Even in Romania, we’re talking about UiPath. How do you see this gap between AI tech giants and the rest of us?

Ines Teaca: The tech giants that you were mentioning have big budgets, big teams, good infrastructure.

Ciprian Borodescu: You know, there is a song, “I like big butts and I cannot lie.” I like big budgets, and I cannot lie.

Ines Teaca: Yeah. I think you cannot compare totally with them. Generally, you know, like, what you could do when you would like to push a product and you don’t know how, I think you need to generate the usage. I mean, people should get accustomed to AI and start using it. So, I think this is the way to try, at least. I don’t know if this is the best way.

Ciprian Borodescu: So basically, first try to have some traction, and then build the budget that allows you as a startup or as a founder, as a CEO or whatever, to have enough money to hire talent and so on.

Ines Teaca: Yeah. But definitely, people should understand it. I mean, what I’ve noticed – but this is not something necessarily just for AI – in general, that if the person for which you are building something, whatever that be, they do not understand it and they don’t use it, then it’s very hard to generate revenues and demand.

Ciprian Borodescu: Yeah, absolutely.

Ines Teaca: I think the key would be somewhere towards generating the usage.

Ciprian Borodescu: And speaking about projects and products, can you give us a few examples of products that you’ve built and what were the biggest challenges there – technical or business – and how did you overcome them?

Ines Teaca: I have built various projects. I haven’t built necessarily data products till now. I would say that I’ve been exposed to the classical projects from a data-mining environment – a classic one like data marts or models or predictions or dashboards. So, these are the types of projects. One of the most important challenges that usually you need to overcome is a good definition of the expected outcome. I mean, a good definition is generating advancement – I mean, you can go further with what you have built, even if it’s a data mart dashboard model or whatever data product – and would reduce the unrealistic expectation, which, from my experience happens sometimes. So, it’s very important at least to try as much as possible to set those expectations. This is why I’m usually starting with this in my mind – a good definition of where we want to be.

Ciprian Borodescu: I think that’s an excellent point. And based on your experience, where do the high expectations originate?

Ines Teaca: Sometimes is a lack of knowledge. Another situation could be, let’s say, objectives that do not go in the same direction. So, people although they tend to say “Yes, we want to do it”, have also other important objectives. So, this is how you are not meeting the ultimate focus and objective inequality. I think these are the most common that come to my mind. Yeah.

Ciprian Borodescu: And what are the three key points you’d want entrepreneurs or executives to remember when thinking of investing in developing intelligent products?

Ines Teaca: As we discussed earlier, I think you need a budget. You need an audience for usage, a team – which these days should definitely include data specialists with the right tools and support. I think you need money to build intelligent stuff, not necessarily a lot of money, but you need a budget. And you need to hire good specialists, and good specialists aren’t all the time students or cheap resources. They are very qualified resources.

Ciprian Borodescu: With years of experience, yeah. I can testify to that. That has been our experience as well. We touched a little bit on this subject, but I want to pick your brain on the diversity topic. Where do you see things heading? Personally, I feel like there are more and more women involved in STEM, which is amazing. However, it still feels that we need to be intentional about it and be on the lookout for women that are interested in a technical career. And I’m wondering, based on your experience, how the local ecosystem feels in terms of diversity? Where should we do a better job?

Ines Teaca: To be honest, I haven’t studied the topic from an ecosystem point of view. I mean, it could be interesting to do it wisely. I generally agree with your point of view. I mean, you really need to be on the lookout. I mean, in Romania, at least, women do not speak up easily. I think it could be also something cultural. We, Romanians, aren’t taught in school to speak up, to express our opinions, to accept different opinions with arguments. We aren’t taught this in schools, so we aren’t accustomed. So for diversity, this is a challenge, indeed. You really need to have on the agenda to have a woman in your team. However, in the IT sector, compared to the European Union, I think the last figures were quite good for us, and this might be due to the fact that in the latest 30 years, or at least before, we used to study all the STEM disciplines, and therefore since we, as women, I need to confess we are more responsible, we studied. So this might have helped the figure. However, in looking broadly at Romania, I would say it’s a long road ahead. I mean, in terms of diversity, we are learning what this means. Firstly, we don’t know how this impacts us all. It’s very hard to change old habits. I still think there are some old habits that are present not necessarily in the corporate environment in which people are more educated. But I would say from a larger view, we are on a road to improvement.

Ciprian Borodescu: Yeah, absolutely. And I think there’s a difference between women involved, even in data science, 10 years ago when you started with this, versus today. I mean, just think of meet-ups – R-Ladies – organized 10 years ago. I mean, it would probably be you and another guy or something.

Ines Teaca: Well, 10 years ago, to be honest, there was Valentina Crisan, who organized the first big data conference, I think. So, we weren’t that many, but we were. Generally speaking, yes, there were less women who were assuming a public image as a data scientist, as a data analyst, so it was less common. But it’s important to have such voices, different voices. This helps us a lot.

Ciprian Borodescu: And how can we help? I mean, we as startups, you as a corporate, universities? Where do you think we should act?

Ines Teaca: So, for me, how I approached this was mostly thinking from a need I had. So, I’m no longer such a young lady, so the opportunities for learning are less. I started this participation in the Slack R-Ladies and I discovered a lot of women who were openly sharing stuff. So I said, “Okay, I could do that. And maybe I could find some other women who would gladly come and share interesting facts.” So this type of approach, I think, it’s useful and it’s something I’m planning to continue.

Ciprian Borodescu: Yes, see, but in your case, I mean, this is exactly what you need to do and, in your case, you were proactive. How can we encourage this proactiveness?

Ines Teaca: You know, I wasn’t proactive. I was actually encouraged. I had the luck to have a manager, which is a woman, which has kids, which has a Ph.D., which likes to put her team in a good position, so I was encouraged.

Ciprian Borodescu: Interesting. Okay, nice.

Ines Teaca: Not necessarily to do the R-Ladies specifically, but generally encouraged to speak up. So I think this is important, to find other women that are available to encourage you.

Ciprian Borodescu: I like that.

Ines Teaca: And then, there were, of course, some other opportunities. But yeah, I think it’s important to help each other, we as women, and of course, we as humans.

Ciprian Borodescu: It’s super important. Role models, exactly. And it’s super important to also have leaders, in general, because a leader actually encouraged you to get out of your comfort zone, and then you became a leader by opening up the Bucharest chapter of R-Ladies. And now you’re doing the same thing for other ladies, which I think it’s amazing. This is leaders creating other leaders. I love that.

Ines Teaca: Thank you. It sounds so great.

Ciprian Borodescu: Awesome. So what’s your plan for the rest of the year?

Ines Teaca: I have a personal project on online training, which I’m planning to finalize. I mean, I really hope to finalize it this year.

Ciprian Borodescu: Okay. Okay, you have to tell us more. You’re not gonna get away just with that.

Ines Teaca: Yeah. I’m doing an online course, which I started a long, long time ago before the R-Ladies. And it’s a course on Excel and I hope to finish it because it has three parts, all the levels, from beginner to advanced. This is something personal. Then, I want to continue with the R-Ladies Bucharest. I think this year we are continuing online, unfortunately. I’m still reluctant to go offline. I know there are some possibilities but, in the context, I’d prefer to have it online on Zoom.

Ciprian Borodescu: Yeah, of course. Yeah. And I think even if this year continues online, I’m sure that events and meet-ups will come back next year. Because, you know, we are social creatures, we need to interact with one another and mingle, and network, and stuff like that. And I’m sick and tired of Zoom, and probably everybody’s like that.

Ines Teaca: Zoom, Teams, Google Hangouts.

Ciprian Borodescu: Yeah, exactly. Exactly. Awesome. Cool. So for the final special section on the podcast, lightning questions and answers – a series of fun short questions that you have to answer really, really fast. Are you ready?

Ines Teaca: Yes.

Ciprian Borodescu: Okay. The last book you read.

Ines Teaca: Atomic Habits by James Clear.

Ciprian Borodescu: Oh, that’s a good one.

Ines Teaca: Yeah, with good advice.

Ciprian Borodescu: Did you read that a while back, or is it fresh?

Ines Teaca: It’s not that fresh. I have other books reading now, but this one is finished. I really enjoyed it because it says something that you need to have all the time a reward, so I felt connected with this advice that James is offering.

Ciprian Borodescu: Yeah, especially when it’s connected with a habit and you want to form that habit. You have to have kind of like a reward because otherwise, it doesn’t stick.

Ines Teaca: Yeah. It’s very interesting. I don’t know if you remember, or I suppose you read the book. It’s that 1% that actually makes a difference. And it’s amazing. Like, okay, 1%. So this is how I started to bike because I bought myself a bike during these times and I started to ride it.

Ciprian Borodescu: Nice! Good Job! Awesome. Favorite woman personality. And I’m also going to ask about a man personality, so you can answer both.

Ines Teaca: So for a woman I really like Michelle Obama a not.

Ciprian Borodescu: Okay. Did you read her book?

Ines Teaca: Yes. Yes, of course.

Ciprian Borodescu: Absolutely. Awesome.

Ines Teaca: I mean, the book inspired me. I saw also the Netflix documentary. It’s a great story, as you said earlier, about what I’ve managed to achieve. I felt somehow connected with some things that she managed to be challenged on and to be such an inspiration today. It’s awesome. We need, as I said, a lot of role models. It’s important to have them.

Ciprian Borodescu: Yeah. So you mentioned Excel. I’m just coming up right now with this question. Excel versus Tableau.

Ines Teaca: I would say Excel. Excel has also this part which developed in the recent years with PowerPivot, with Power BI, so I’m thinking more towards that. And, of course, it’s also something regarding everyone in a corporate environment to use Excel. But Tableau is also nice. But if I have to choose, I would say Excel, it’s wider.

Ciprian Borodescu: Okay, and now a tough one. Python or R?

Ines Teaca: It’s easy. R.

Ciprian Borodescu: So the bonus question – I have to have this for every episode, but for you a special one: Tik Tok or Instagram?

Ines Teaca: Oh, I’m a Tik Toker, maybe, mostly due to my daughter.

Ciprian Borodescu: Okay, so she is a Tik Toker, not you. I thought kids were supposed to be on Tik Tok because their parents aren’t there.

Ines Teaca: Yes, but I’m a cool mom, so I’m there.

Ciprian Borodescu: Awesome. Awesome. I love that. I love that.

Ines Teaca: Actually, she’s quite passionate about it and she explained to me how she did over 1000 followers so she could do a live on Tik Tok. I was amazed.

Ciprian Borodescu: Oh, my God. She’s a little influencer or something, right?

Ines Teaca: I don’t know. I need to follow her.

Ciprian Borodescu: Ines, it was a pleasure to have you on. Thank you so much for sharing your wisdom with me and with us. How can people reach out to you for ideas and comments?

Ines Teaca: I think they can reach me via email or LinkedIn. Ines Teaca on LinkedIn or inesteaca@gmail.com

Ciprian Borodescu: Awesome. Thank you so much, Ines.

Ines Teaca: Thank you.