How Data Scientists Can Thrive in the FMCG Industry

Adel Nehme:

Anastasia Zygmantovich it's great to have you on the show.

Anastasia Zygmantovich:

Great to be here. Thanks for inviting me. I'm actually quite frequent listener to the podcast. I'm very happy to be here.

Adel Nehme:

That's really awesome to hear. It's really a huge honor to hear that. So you are the Global Data Science Director at Reckitt. I'm very excited to speak with you on what it means to drive value with data in the FMCG space. But maybe to set the stage, walk us through a bit about your role at Reckitt and maybe for listeners who may not be aware, what does Reckitt do? Some of the products that Reckitt produces that listeners may be using that are not aware of.

Anastasia Zygmantovich:

I'm sure happy to do that. As you mentioned, I'm Global Data Science Director, so I'm responsible for AI, machine learning and all technologies related to data science. But also I think it's good to set the stage about FMCG in general and Reckitt too. Probably most of the listeners you do not know the name of Reckitt, but I'm quite confident that most of you have some of our products in your bathrooms or kitchens, because of Vanish, Airwick, those are quite well-known brands and I think that's what makes FMCG so relatable because you really know the products and you have used them.

Adel Nehme:

That's really great. So I want to really set the stage for our conversation by really focusing on what it means to drive value with data in the FMCG space, kind of the key components of driving value in this space. You know, you mentioned here, you know, a lot of the products are relatable. This is a relatable space. And that's really the beauty of the FMCG space and kind of working in data science here in this space. So we've all consumed record products at one point in time in our lifetime. So... With that said, what do you think are key components for driving value with data in the FMCG space?

Anastasia Zygmantovich:

Maybe I'll start with some foundations because what we have to get before you drive some value is data itself and it means that you have to have proper data governance and the way we approach data is some of the reasons might know it already so that's fair principle which means that we want our data to be findable, accessible, interoperable and reusable. So that's very important for us as data scientists and the rest of the IT community can use data in that way. And what's also important to drive value with data is strategic alignment. So we have to make sure that very senior leaders also support our strategy and that we create buy-in. So that's an important aspect. And maybe the last mile that should bring it to life is to embed it into business processes.

Adel Nehme:

Okay, that's really great. And I'm excited to unpack a lot of this, a lot of these components, you know, from data governance to getting buy-in from leaders and making sure that, you know, data is embedded in decision-making and business processes. But first maybe to get a better lay of the land, you know, there's a lot of business challenges in the FMCG space that can be solved with data because it's such a relatable space and a lot of really relatable datasets. Maybe can you provide some examples of the data science use cases that unlock a lot of value in FMCG and how you've seen that play out at organizations such as Reckitt.

Anastasia Zygmantovich:

Sure, let me touch on some of most common use cases for FMCG but also some a bit less common for FMCG and more specific for record. I think that might be quite interesting for your listeners. So the biggest use case is definitely on revenue growth because again as a business we have to show that our revenue grows year to year and to make it happen we look at the pricing so we want to make sure that we are able to do price ups in an effective way. That's a big topic actually this year for whole of FMCG with inflation and you have to make it quite often and in an effective way. And the second part of revenue growth is actually promotions. So basically how we execute discounts in the stores to decide which store we should do, which kind of discount, what is more effective, what is less and this way we can actually execute more effectively with less amount of money. So that's the biggest use case usually FMCG has on revenue growth. Another one is definitely demand planning, and if you think about FMCG, we actually very big manufacturers. So we have dozens of factories to produce our products and they are located in different regions. So it's a huge exercise first to purchase all the materials to produce those products in enough quantity to actually deliver what we're going to sell but also we have to predict how much we're going to sell and make sure that all of that alliance to what we're going to produce and later how we're going to ship it. So this is where data science plays a huge role and it's one of the biggest optimization cases actually. It's not just prediction, it's about how to make whole value chain work. So that's a second big use case. Maybe a bit different use case on shelf image recognition. So we want to understand how we and you look at the shelf, we want to make sure that, for example, our share of shelf represents our actual share in the category. And to track it, you don't want to basically send people to the stores to write down and measure amount of shelves that we have, because that's a lot of work. What we do is we take pictures and we try to see automatically using image recognition what is actually our share of the shelf. And this case is quite interesting because in some countries, Brazil. Shelf's alleys can be quite narrow, so it's very difficult to take a head picture, and it means that it goes on the angle, and it's very difficult for analyzing this data set to see what actual share of one picture is getting smaller on the corners. So that's quite interesting use case we have on that.

Adel Nehme:

That's awesome. And definitely we're going to unpack a lot of these use cases in a bit more detail. But maybe let's start off with the key components of making data science successful in the FMCG space. You mentioned here the proper data governance, strategic alignment, and making sure that data science is embedded in the decision-making process. So maybe start off with data governance and making sure that data is accessible, findable, usable. Walk us through how you approach that as a data leader and maybe some of the best practices that you can share in making sure that data is usable and accessible and findable.

Anastasia Zygmantovich:

So that's one of the most difficult parts. It's not part of my regular job because it's part of data governance team. And those are the basics we were missing in records and those are basics many fast growing companies are facing. And the way we try to approach it is by data mesh concept. So we want to make sure that we have some big enterprise datasets which we have responsibility of global teams. So those will be for example, sales, material, big datasets, but then on top of that we have smaller datasets in different layers for which different people are responsible. So we do have the structure for the data governance to make sure that, for example, definition of even share of shelf which we were talking is the same in multiple countries and basically the way to do that is to do two things. First, create this huge buying from the top leaders to make sure that there is a big data-driven push to actually get it to happen. The second part is actually to put people responsible for that on every stage where we touch the data.

Adel Nehme:

That's great, and you mentioned here, you know, you approach the data governance component through a data mesh framework or concept, right? You know, in a lot of ways, data mesh is still a nascent idea, and a lot of companies are trying to implement it. Maybe what were some of the challenges when trying to implement the data mesh, and how have you approached that when trying to solve for it? What are some of the best practices you can share about implementing a data mesh with listeners?

Anastasia Zygmantovich:

To be frank, we are also at the beginning of this journey and the biggest barrier I think we face, but I believe many companies also face, is to build this understanding of people. What is the value of data mesh? Why do we do that? And the easiest analogy, I think is microservices. That's the way we usually explain it, to say that you split basically big components into the smaller ones to make the modules, which you can basically mix and match. That's the same as with any transformation. You have to create buy-in, you have to educate people, you have to make sure that you spend a lot of time on change management. And I think this part is actually quite complicated and if you look at it together with data governance, that's actually whole foundation you need to have a successful data strategy.

Adel Nehme:

Okay, that's great. And this ties into, you know, the second component that you mentioned, which is, you know, really strategic alignment, leadership buy-in on data initiatives. Walk us through maybe kind of the journey of getting executive buy-in here. What are some of the, you know, key components of getting executive buy-in? What are some of the challenges that you face? I'd love to understand the story here of getting executive buy-in that you've had throughout your journey at Reckitt so far.

Anastasia Zygmantovich:

So I think the easiest way to create any kind of executive buying is to prove the value. So this is basically the start of the conversation. So it's two things. What value do you deliver? But what is the cost of not doing that? So that's the second thing. So the way we talk about, for example, responsibility, that's the second option, that we have to do that, because the cost of not doing it is very high. In case of data governance or appropriate data structure, data quality, all these things, they are a bit less understandable in that sense, so the easiest way is actually to talk about specific cases. Like for example, we're speaking about the revenue growth, but actually to make it happen, you have to get the proper data set. You have to get the data from 50 different customers, which means you have to have proper structure for data governance to get it done. Then you need to have proper data quality. And if you don't have that, it means that you might produce some results, but they might be actually harmful if you interpret something which is not good quality. So I think what speaks the best is actual cases and conversation based on prospective value.

Adel Nehme:

Okay, and maybe in a lot of ways early in the journey, there's maybe some resistance or maybe there's a need to prove value of data. What is a good framework to approaching, you know, proving quick value with data if a data leader is starting off within an organization? What is a good framework for quick wins maybe a data leader can get when driving value with data and when trying to showcase the value of data within an organization?

Anastasia Zygmantovich:

So I think what works in our case is actually using AI as quick wins, because this is the area where there is a lot of interest, a lot of hype as well. And what we did is to create some kind of lightweight framework to actually collect those cases and deliver as fast as possible to see where the value is. And that really speaks because then to actually make it happen long term, you need all these foundations, but actually, to unlock it you have to approach data analytics as a whole big space with all the foundational parts.

Adel Nehme:

Okay, that's really great. Now, I wanted to switch gears here because we mentioned and talked about data structures and data governance and leadership and buy-in and making sure that data science is embedded within the business processes. I'm going to want to talk about how to embed data science and business processes in a bit more detail. But what I want to discuss a bit is really how to build a high-functioning data team as well because in order to deliver that value to put data science in a business process, you need to be able to have a high functioning team, right? And that starts with the type of people that you hire, the type of talent, the type of skills within a team. So I'd love to get first from you what you think is a high impact data team in the FMCG space.

Anastasia Zygmantovich:

So maybe I can start with organizational part of it and later go to like talent part of it because I think it takes both to actually get it happen. So on the first part in the organization, it really depends on maturity of the company because sometimes it really works when you embed data scientists into business teams and so then they really understand the domain and they can deliver the value. In our case, it actually didn't work. And what happened is people quickly realized that they don't have career path in whole region, that's clear, there is no second step. And what we decided to do is actually to build a hybrid model to integrate data scientists into the chapters, so this way they have access to various business problems generally. And I think that's important to expose people to different business problems, not just technical ones. So that's one of the aspects of that. And second, on the talent specifically, you need to build a team who can do various things. AI is going so fast, it's very difficult actually to hire a person who can do everything. I don't think anymore it's possible to have a person who understands statistics and can write the perfect code in Python, can do MLOps and everything. We don't expect that anymore. It also means that you have to think which part of data science skill can be outsourced to different organizations. In our case, we think about MLOps this way, a separate team to create a framework for us to execute those data science projects as fast as possible, but we do not expect data scientists to have the skill to do whole MLOps themselves. So basically you have to vary the skills and divide teams in such a way that you can execute as fast as possible, because as we mentioned in the previous point, to prove the value you have to execute a lot of cases and to actually show and find actually these cases which bring you a lot of which is a great impact.

Adel Nehme:

Yeah, and let's break that down a bit. I want to really deep dive into that organizational part of things. I think a lot of organizations are still trying to identify what is the best organizational makeup or kind of structure of a data team. We've seen, you know, you mentioned here an embedded data scientist model did not necessarily work at Reckitt. Other organizations have to have like a purely centralized model. You opted for a hybrid model. Maybe walk me through the journey of making that determination. And also you mentioned that really key component of creating growth pathways for data scientists. Walk me how that looks like right now at Reckitt. How do data scientists grow within Reckitt? And how did you create that laddering journey?

Anastasia Zygmantovich:

Sure, so actually two transformations happened for us last year. And the first one is on the product organization. So a whole IT community transformed into this product way of working, which I think is industry standard. And again, this is a proper way to give people the right careers. And the expectation for the product manager is that they're kind of CIO of their business capability, which works in a great way because then they also track value adoption, What also a product manager has is a team, which we call squads, to actually deliver those capabilities. And we see data scientists as one of the members or a couple members in the squad. We usually have, if it's a data-related squad, there will be data engineers, there might be some visualization specialists, there could be some DevOps, so it really depends on the project. And this way, a data scientist has basically first family inside of the squad. And the second family is actually chapter. So in case they want to progress in different way, they want to learn different domain, they can still do that because they are part of global data science team. So that offers this flexibility.

Adel Nehme:

Okay, that is really awesome. And in a lot of ways as well, we talked about the skills component and the talent component. We mentioned that you don't opt for like a one size fits all data scientist, right? There's a lot of different roles that occupy different parts of the data value chain within Reckitt. I'd love to understand from your perspective, what are the essential roles that make up a high functioning data team? How do they play together in terms of a collaboration, in terms of the data value chain as well.

Anastasia Zygmantovich:

So I think it really depends on the company. I can only explain how we work or how I worked in my previous companies as well. So you definitely, like what works for us is this chapter approach where we have, in this case, three chapters. One is data science, but we also have data engineering and data visualization. And this is specifically for the career paths. So that was the main reason to make sure that people have a second family and we can determine how they work. We also try to make sure of the fair concept and for that we actually have specific roles. We call them data product managers. So the main goal of this person is to make sure that data is fair. So that's exactly the role which is responsible for that. But also we have data governance teams. So these data product managers, they work very closely with data governance. So if you look at foundations, which ensure that we have right quality of data, but to actually have data in the right place. We also have the platform team to make sure that it's in the right cloud, we have certain operations, we have DevOps, all these things. And of course we have Data Architect, which oversees all data products with a team of architects responsible for specific business domains, which again puts it into smaller pieces. So yeah, so that's a solution architect for data specifically, a platform team, data governance team, data product managers and then the chapters which is engineering, visualization and data science. Pretty much like that.

Adel Nehme:

That's really great. And when thinking about the makeup of a team and the impact of a team, I think a big challenge data leaders face, especially in 2023, because you mentioned here, it's an inflationary period. A lot of organizations are looking at the ROI of different initiatives that they're trying to put in. It's trying to understand what the ROI of the data team is and trying to quantify it, right? How have you approached the ROI question from the data team and maybe share some best practices with data leaders on the call here? that could benefit from trying to understand how to calculate the ROI from their Theta teams.

Anastasia Zygmantovich:

I think first... first thing is to make sure that you do that so that you embed it into the process that the first pretty much step you do when you discuss business case is actually to think about this value and then it's really it really depends on the products and every the easiest way to explain it is to go back to the use cases so when I was talking about revenue growth so that's obvious when you look at the price ups it's a question how fast and how high you can do the price up so you can actually earn more so it's simple like that case of promotions is that you do not overspend for discounts where you don't have to so again it's certain saving of money so it's either you earn more or you spend less. In case of demand planning for example that would be rather the cost of failure to actually deliver those products to the customer so it's a bit more tricky. In the case of this shelf image recognition that's quite interesting case because this one is related to the technology cost because is quite pricey, especially for small countries where we don't sell for millions of pounds. In this case we actually try to see if we can build something in-house using standard components which will be much cheaper for the smaller markets. So in this case it's a kind of technology saving but also a possibility to bring these tools to the markets which otherwise cannot afford them. So in this case I think it really depends on the use case.

Adel Nehme:

Okay, so that's really great. And maybe let's focus in on one use case, right? I was really interested in the demand planning use case because not only does it require, you know, really strong kind of data science jobs, but it's also a very strong optimization problem, right? Maybe walk us through the different data sources that you need to think about when trying to create this type of use case. And how do you approach embedding it in a business process? And what type of insights does it inform the remainder of teams that wreck it?

Anastasia Zygmantovich:

To be frank, this optimization issue is actually the business problem. It's not a data problem in the sense that it really has to align between different teams, where some of them local and some global, plus it goes across multiple organizations. And this sense, first thing to do is actually to establish the business process to understand who is playing which role where. And then for each actor in the business process to identify how they do that and what kind of data they use. which you already have, obviously you have to check if the data quality is right, if there is ownership, all these parts. Another thing which you could do is actually to look a bit wider, because again, if you have proper ownership for the data domain, in many cases business owners, they're not even aware of some datasets, and it makes sense to actually expand the datasets you're using to what is available. And then to think how to build this use case end to end. Our value discussion. This is a huge optimization case and if you wanted to crack it from the beginning to the end it will take you years probably. So it's important actually to decide which part brings the most value because if the biggest business problem is that we don't produce enough products and that our let's say demand predictions are low quality then that's the space you have to address it. If the problem is we cannot buy chemical components for those products we have a separate product for specifically that. So you really have to put it into smaller pieces but where actually these big corporations either win or lose is actually to build these connections to make sure that you don't stay in silos and you think about the future also to integrate it back.

Adel Nehme:

That's really fascinating insight and really connects to the aspect of embedding a data science solution in a business process, right? Especially for a use case such as, you know, demand optimization, optimizing demand. There's a lot of different teams that play into this, you know, from supply to the, you know, planners to a lot of people who work with retailers to put products into market. Maybe how... Walk us through the process of embedding, you know, a data science solution such as, you know, demand planning into a business process. Who do you need to work with? How do you approach these conversations? And walk us through some of the best practices that you can share here as well.

Anastasia Zygmantovich:

So think. Again, you always start from the beginning. So like personally, in data science, we have a checklist of things which have to happen before we start. And I will just talk about the value. But second is actually how do we embed it into the business process? And that's actually responsibility of business. So when they come to us with a business case, first thing we told them great case, but let's decide together before we start where in business process is going to land. Maybe a good example again is demand planning. case when our supply team came to us we want to improve quality of forecast prediction and first question was like in which meeting you're going to use it or we're going to build and first was like oh i'm not really sure maybe somebody will use it and i was like no that's not gonna we have to really define from the beginning like how often this meeting are happening if it exists you can just reuse the meeting but in that case it actually didn't so it means that you into what you're trying to achieve. So that's important part. Sometimes these processes are very big and often we actually have choices, data science, either to run it as a side project somewhere or to embed it into something bigger, but then we have to wait. And my choice is always to embed it into something bigger because then it becomes a part of the business. So we can run some pilot, but with this thinking that at the end it has to land in the same space. For supply we use a system, which I will not name here, but it's company-wide system. And recommendation is obviously to try to put some additional arguments inside of it, because this is one space where people go, they open the system and they do everything there. Every time you try to give person additional system, your adoption drops, and it actually drops for both tools, for the main one, which we spent millions actually to deliver, but also for the tool you're trying to develop. So user experience aspect is extremely important. And this is, I think, where a lot of companies missing. You have to really understand who your end user and what will be the experience for them.

Adel Nehme:

And in a lot of ways that initial conversation when you're trying to understand from your business partner, how are you going to use the tool that I'm going to develop? Does that inform the actual development cycle and how that tool looks like? Because it's going, because user experience, as you mentioned, is extremely crucial aspect of making sure that there's strong adoption.

Anastasia Zygmantovich:

Definitely yes. And also if you look how you deliver data science, it's a life cycle which repeats itself, right? Hopefully that should be this way in sense that you never just frame the case and you develop it till the end and then you tell the user there it is, use it. So what is expected is that you do multiple iterations with both and you just explore the features and you consult with business. Does it make sense for you? And if the answer is that they're going to then you have to adjust with it because the end goal is not that you just develop some tool, is to make sure that there is adoption and value. And if there is no adoption, there is no value. So in this sense, we try to also embed this process of talking to the business constantly into any data science project.

Adel Nehme:

That's really great. And related to this is, you're really through these conversations, right? Like adoption of data science, making sure business stakeholders use data tools. I think that requires some level of data culture within the organization, right? Not just buy-in from leaders who are strategically aligned with the importance of becoming data-driven, but also middle managers, individual contributors, who may have years under their belt developing their own gut feeling and their own gut instinct when it comes to how to approach business problems. How do you approach that buy-in component with your business stakeholders? And what do you think is the most critical factor data leaders need to look at when trying to develop a data culture and their business counterparts?

Anastasia Zygmantovich:

Currently what helps a lot is the fast development of AI, because in this case, it's much easier to convince that if we don't do anything in 10 years, we might simply not exist. So I think that that's a very strong argument we have. And obviously to approach any kind of data literacy effort is two ways, and actually you have to do both. It's like top down and bottom up. So top down, that's very important because otherwise it's very difficult to do role modeling inside of organizations. But the bottom-up actually requires much more work in the sense that you have much bigger user base to train, and the easiest way to do that is to define personas which you're going to train because not everybody needs the same knowledge of data science or even data governance data in general so I think basically we just need like the way to do that is to define those personas and actually create learning paths for them and expectations but also embed it into evaluation process. So we have like yearly reviews of performance for example and some of the skills are solved. So data is actually a perfect skill to put into this performance review and it's important that you make it measurable in some way. So I think that that's a key component because just to run the trainings it's never good enough. You have to make sure that people really feel it. They have some experience hands-on and one of the things which we basically allow people to go to kind of show and tell sessions where we actually explain how we do things. Sometimes it's quite in depth so not everybody would understand but it's quite exciting for them and we encourage people to come to us with ideas so in this way we create a lot of buzz and we call it like FOMO in different organizations. So they want to do these things and mainly if we do it again with AI because it's much we always of course convince them that we have to get foundations and basics and it's not just AI it's a lot of other things.

Adel Nehme:

That's really great. And you know, definitely you mentioned here kind of harping, like, riding the wave of AI, right? Of the AI hype that we see right now, especially with large language models and tools like JAT GPT. Maybe how do you think that there has been a culture shift recently within organizations when trying to think about data science and AI because of the recent boom that we've seen on AI? How do you see, how have you seen that play out? Have you seen that there has been a difference, for example, in how people think about data and AI recently?

Anastasia Zygmantovich:

It's actually funny that you ask right now because next week I'm going to our general executive committee. So this is a team of our CEO basically to talk about it. And one of the slides I have is actually about how the ChatGPT hype accelerated investments into AI. So actually it's a huge impact everywhere. Also on the senior executives, because if you have hype, you feel that you have to invest more. So we feel it. Everybody feels it. And I found some market research about it. It's actually quite a proven situation. And I think this is great because again, sometimes it's very difficult to sell value of data if there is no kind of exciting things happening around it. Sometimes it's difficult to sell, let's say, a dashboard. Sometimes it is exciting, sometimes it's not really. But when you talk about AI or ChatGPT, something which everybody touched, I think that's the biggest impact of charge GPT that everybody has access to. They can see that it's really working and it almost a magic for many people who are not technical. So in this sense I think it creates a huge buy-in for data but also for AI.

Adel Nehme:

Yeah, in a lot of ways, you know, ChatGPT is maybe the first consumer application that has put AI in the hands of, you know, millions and millions of people, like really strong AI, and this is what's really fascinating about it. One, you know, additional, you know, question here on that is that we see a lot of additional investment in AI right now because of the ChatGPT hype, right? But do you think that... Do you think that could take away maybe attention from, you know, executives and leaders on, you know, low hanging fruit, simple use cases that can really improve data driven decision making in the organization because we want to start developing, you know, more advanced AI systems within a specific team or company rather than, you know, really focusing on descriptive and diagnostic analytics. Have you seen that there is a trade off here when thinking about prioritization of data science and AI use cases?

Anastasia Zygmantovich:

I think you're very right and even the way we approach it in two things. One is innovation boost, of course those small pilots, which in many cases they come from generative AI. But the second part is like strategic big initiatives, which are actually delivering the impact. And I think it's important to balance it. Actually, we do not struggle with that, but I can see that because there is so much hype, hanging fruits and I think it's important to balance. The way we do that is we centralize the intake of those ideas. So the way we do that is we have like a form where we collect and we meet literally with every single person who submitted this idea. Also to do some education to explain like it's not maybe even generative, maybe it's actually automation, it's not data science cases like that coming in. In most cases they're either not framed well enough or they do not bring that much value. It’s important but also you can centralize. So one of the biggest use cases we have is AI content generation. But if you think about FMCG, we have content everywhere. We have it in our websites, in our D2C websites, we have it in Amazon, on e-com, we have it on the packaging of the products, we have it everywhere. So it's much better to actually centralize this effort to make sure that it plays together well rather than to have five different teams trying to figure out. So what really helps is to centralize innovation pipelines. And that's one of the advices I can give.

Adel Nehme:

Okay, that's really great insight. So, you know, as we near the end of our conversation, I said, what I want to focus on as well is maybe the skills needed to succeed as a data scientist in the FMCG space. You know, we discussed how this space has a lot of relatable data sets and relatable, you know, products that data scientists can really understand intuitively. But you mentioned as well that a lot of projects also require stakeholder management, a lot of mix between hard skills and soft skills. So I'd like to understand if you can elaborate a bit more on what is the perfect skill set maybe of a data scientist in this space, what type of balance there is between technical skills and non-technical skills to excel in the FMCG industry, and how does that differ from any other sector that you've worked in?

Anastasia Zygmantovich:

So I think FMCG, but also other companies which are more like business related and less maybe digital native or technology companies, what we share in common is definitely this requirement on the skills of the data scientists that they understand the business actually. And for example, there is always a choice. You can take some vendor to do data science for you, but we try not to do that for data science specifically for the reason of bringing huge impact, especially like supply chain. I think it's so complicated that you need to retain this knowledge. You really need to get someone who understands it. And in many cases, those people don't even come from directly data science. They actually had experience in supply chain. And after that, they also had some technical skills. So I think a very important skill is actually understanding of domain knowledge. And in the FMCG, we have many domains. We even have interesting case where we did like predictive stability of chemical components and for that we were lucky to have a person who was a chemist actually. So in this sense you really need to have a good mix of people because if you operate as a chapter different cases are coming to you and you have to be able to handle those. So this kind of various knowledge in different business domains is very important.

Adel Nehme:

And what advice would you give maybe data scientists joining a company like Reckitt to develop that business domain who may not have had that business domain and supply chain or, you know, understanding the FMCG space necessarily. What are the type of challenges that you think junior data scientists can overcome here in this situation?

Anastasia Zygmantovich:

So the way we approach it is we make sure that they have as much contact with the business as possible. So sometimes they basically even do like a store checks with people or we try to do onboard marketing the same way as you would onboard brand managers for example. So we try to make sure that we basically expose them, but also if they work on specific business case we expect business owner to spend some time to onboard this person and it's win-win for both because in many cases they want to understand data science better. and we want to make sure that our people understand business better. But generally for data scientists who are just starting a career, I honestly believe that FMCG is a great start, again because it's very relatable. So many of the use cases, they're not that complicated, that you always need a senior data scientist to do that. And it's also a good combination when two data scientists are working together, a senior and a junior. So that's usually how we approach that. And I do believe that it's a great start. Thank you.

Adel Nehme:

Okay, that's really awesome. And maybe to cap off here our discussion on the skills of data scientists, maybe what would be your advice for aspiring data scientists looking to get into the FMCG industry? How did they get started?

Anastasia Zygmantovich:

I think if a person already has some skill sets in data science, then basically they can try to go for specific domains in data science. If they want to learn about specific domains, for example digital marketing, tend to be one of the areas. You can obviously upskill yourself in different ways to go there. But what I usually appreciate in my interviews is this enthusiasm that person really wants to learn about specific domain. get any kind of job but there was actually a lot of intention to make it happen. So in this sense I think that's when it really works.

Adel Nehme:

That's really awesome. So Anastasia, as we wrap up our conversation, I'd be remissed not to talk about some of the trends that you're excited about to see. We talked about chat GPT, a lot of the hype in AI right now. I'd love to see, from your perspective, what are some of the trends in this space that you're looking at currently that especially are exciting for you at Reckitt and FMCG in general.

Anastasia Zygmantovich:

So I think the one I keep the closest eyes is still generative AI, but also what I'm interested in is how it's going to change future work. So for example, Microsoft announced the copilot and I think that's something which will really change how people operate. So in this sense, I am really interested also in hybrid intelligence to see how basically AI can work together with humans. I don't believe that anytime soon we're going to replace people with AI. Very successful leaders, not just in IT, actually those who will be able to use the AI in a wise way and understand it. So I think that's very important. And I'm preparing myself more and more things happening because right now every week something new is coming out. So I think it's extremely exciting.

Adel Nehme:

It is indeed extremely exciting. And you mentioned here earlier in our conversation, the importance of embedding responsible AI into the process of developing use cases. Maybe share your view a bit on how the responsible AI conversation will maybe change within a year or so as these generative AI tools become more and more embedded in business processes. I'd love to get your perspective as well on tools like Microsoft Copilot and what that looks like from a responsible AI perspective.

Anastasia Zygmantovich:

I think this topic becomes more and more important for tourism. First one is more like on regular tourist side because there is more and more legislation which is actually happening also across Europe, in US. But the second part is we are in situation where there is AI everywhere, so it's much more difficult to control it. And it means that if you don't control it now, it will be much more difficult to control it later. And the complexity of responsible AI is the fact that you really need to... across disciplinary teams that should make it happen, plus it is responsibility of pretty much everybody in the company. So I think that's hugely important to start as soon as possible and to create also buy-in with the leaders where the biggest part is actually avoiding the cost of mistake. I think that's how you measure that. But many people, especially young generation which I'm super happy to hear, they are very interested in that. They are really value-driven, they want right thing and that brings a lot of hope.

Adel Nehme:

That is really awesome. Now, Anastasia, as we wrap up our discussion today, do you have any final call to action before we end today's chat?

Anastasia Zygmantovich:

Maybe to data scientists, especially those who just starting their careers, I just wanted to make sure that you think about any use case you're working as something which is related to real life. You can even think about some of FMCG products like Durex, Vanish, how they got into your hands, where did you buy them, is it like online channel, why did you buy that, how content look like, if you bought it from the shelf, how shelf look like. All these things just to before you got the product into your hands, to actually AI, data science, IOT related, and to actually feel what kind of impact you might have as a data scientist, that it's not just like small piece of code, it's actually making whole process happen. So I think it's quite important.

Adel Nehme:

I couldn't agree more. That's a very inspiring message. Now, and it says that I really enjoyed our conversation today on DataFramed. Thank you so much for joining us for the show.

Anastasia Zygmantovich:

Thank you too.