Hello. Hello hello and welcome to data futurology the podcast for data enthusiasts data scientist and upcoming data science leaders to learn the skills that you need to take your career to the next level. We do this by listening to the stories the lessons learned and the trauma of the current industry leaders. My name is Felipe Flores. I am your host. I hope you're having a wonderful week. It is a pleasure. ER to have you on the show. Thank you so much for listening. Today. We're speaking with Tony group Nur he is the general manager of analytics insights and modeling at sports bed.com. The you Tony has had a fascinating career with a blend of analytical roles and Commercial roles and also a stint as an entrepreneur for a few years. So very rich experience. He tells us about his journey his lessons and how he got to where he is. I hope you enjoyed the episode. Hi, this is bleep a today. I'm sitting down with Tony Tony. Thank you so much for making the time. I great to have you on the show. Thank you very much for having me at the beginning of the interviews. I always like to ask guests about how they got started in data and their professional background or the journey that they've had so far. What did I Journey looks like for you? My story is sort of a bit of a long-winded sort of long and winding so the story The coaster so you sort of points as to how I got here. And then essentially I think it probably starts all the way back to my last set of years. I know actually failing math school. I'm sure that my my messed very much last the fact that I've had ended up in such a sort of math so far. So I was outside of that and that's as pretty as decent at school. I studied at University. I studied management and e-commerce and this is the stage where this is before Google was even invented or Facebook didn't exist. See comments was a little bit of a redundant things to study and in management was great, but no one was going to really hire you as a manager. Straight out of school. So I took a house a prince to roll with a commercial with a consumer electronics company after a couple of years was that I was actually horrible the sales. I think that the business probably worked that out by this feature as well. So I remember getting put on to this job where we just taken this contract to be the supplier of batteries consumer batteries, your double a batteries and whatever for about 250 different supermarkets around I was living in New Zealand at the time and what they need it was they needed someone to draw 250 planograms. So planogram is spicy saying this is where you should position which the batteries in each of the shops. They wanted someone to draw 250 planograms and send them out to the mission sizes. I got to about number three or four and then realize this is ridiculous and it's quite a science behind how you build these planograms, but this was a ridiculous task. So I ended up taking my sort of limited XL knowledge and taught myself sort of Visual Basic and I automated the whole task. This is before I even knew things like data science or anything existed and I made the whole task break. In this application and it was a pretty low level application built in Excel but application nonetheless that took into account sort of the size of the planograms and the size of the area and took into account the commercial sales and they're in the region and it was it was a little bit of a model that women behind that program is no spreading a model and that was used so that we could essentially whenever we wanted to we could create a new planogram automatically and send that out to save me having to draw out 250 different planograms. That's when I started to realize that I sort of like that type of Work and and I had a skill for taking a my technical skills whenever that great but itís still taking things and automating them and understanding how numbers work and putting them together. My next role. I decided to move I got out of the sails says was as I moved to Dublin I took the day I arrived in Dublin. I got off the plane and they get famous place to get an interview. They were looking at was a property company and this is 2007. So right in the height of the Celtic tiger the Dublin economy was going crazy and I go this interview with a D company, so this is a mess of property company that owns and a number of different properties. They have no systems as to how they managed all these properties and understood how many properties they didn't even know. She how many properties actually had let alone. What was the status of all the properties and what was all the information so they wanted someone to manually put all this into Excel spreadsheets and it was an eight-week engagement. And so after I looked at that for a couple of days, I then went well this is without knowing anything really about databases or anything at the time I said, this is a job to actually create a massive database and something that The team can access and as they need so I didn't know how to do this at the time that I pitched this to them and they said yeah, that sounds fantastic. And you do that for the next 12 months. So tune an eight-week engagement in to 12 months did that and then by this by the time I got to the stage, I realized that this was probably where some of my skills they around sort of the more analytical database and type stuff. I think decided to move her to London. And so now by this time we've got past the sort of booming economy and now we're straight into the sort of GFC and its job was absolutely Literally impossible getting an interview for a job was impossible leaving finding a job that was advertised was impossible. So I what I did was I wanted to double back on my e-commerce to go. So what I did was I actually started from the couch of one of my friends houses. That was then Lisa stay there for free. I started a website that sold of all things dog bits so beards for dogs, which about the only reason I chose that was because I Found A supplier that was able to supply but I wanted to understand more about digital one understanding more about selling. Stuff online and understand that whole world but sham but obviously had an interest in in but have moved a long way since I studied it back to school and part of that. I got really quite heavily into digital marketing and pay per click advertising Etc and actually ended up writing a few sort of once again still quite basic but in Excel, but a few algorithms and tools that actually significantly enhanced my ability to run my pay-per-click campaigns and to bird on keywords as part of that. I actually got picked up by One of the search marketing agencies whenever I search marketing agencies in London, they found out some of the stuff that I was doing and they wanted me to bring that into them. I know selling the website that I created which I didn't sell for that much but I sell for a profit at least definitely wasn't enough that I was able to retire I then was worked for the agency and then by that stage I was fully sort of in this world where a was much more understanding that my skill set was more sort of in the analytical and how you bring that directly to the customer and build tools that help to serve that and ins that are useful and so after all that I ended up coming to go all the way back to New Zealand. I went to Australia in Melbourne and spray them now and a ended up getting a role as an analyst at sports bet which is once again where I'm at. Now, this was about seven or eight years ago and at the time sportsbet had about 200 employees. I know getting a call from a recruiter basically said do you want to do work on analytics in a company that does Sports and is heavily digital and I said all those three things tick the box so I took the rollers. Spit it was very much a sort of entry-level analyst role but it's time there wasn't really many analysts here that has grown over time and grow size of the business has grown substantially almost probably from about 200 people to about 800 people now and over time the needs for analytics has grown. So I've been able to grow some analytics teams and moment. I look after the data products team here in terms of the we look after all the data science and advanced analytics and sports modeling that we do has been an interesting Journey. Like I said, it wasn't probably the typical Journey into enter data and analytics and I come at it from a slightly different Becker. But I think one thing to note is that I think everyone that's sort of worked here and better analytics and myself included has a slightly different Journey as to how we got there and there's no one pass that always works. That's one of the things that I really enjoy about having these conversations that everyone has had such a unique and different parts and it definitely sets you up for I guess on one part you have to be very passionate very driven to sort of keep improving and growing your skill sets in an area that there hadn't been a part. Right in front of you that was clear. But the other side is that your Origins give you a different sort of advantage and different focusing capabilities that you bring out in Europe later career such as now running your case. Would you say that your origins or you may be your first specialization? Would you put that as marketing or e-commerce as your first one in your mind? Yeah muscles probably dissing on the technical analyst or comment from the technology side and others here to learn about. A lot more of that especially in my current role or even is I wouldn't even call it nicely from the better science side my skills come from I think I understand the coming from the more commercial PSI is that I understand that and having a sort of numerical and understanding of numbers. Let's say which is obviously not a very academic understanding to the point of failing Master my last year of school but a numerical understanding of what analytics can actually brings in customers and what can she bring to a business as if that's where my skills lie. Definitely not the far from the most Unless the world or and I'm going to tame a data scientist all you know, significantly more about data science and about doing cool things with numbers than I do. I think my ability is to package all that up and actually deliver it and give some commercial sort of Acumen to it. Very very interesting and how has that strength played in your career? What things would they have enable you to do early on and I'm going to have you seen this the benefits of having that as your strength or focus them on the main strengths. Is that mental point the One of the examples is given before about some of the stuff that I did like I kept on reiterating. They went heavily technical models or the most of what I spoke about at that stage was built some sort of an Excel or access using sort of Visual Basic what they were were applications that were built with a good commercial understanding of what they were trying to achieve. So therefore while the tools might have been primitive or the application themselves might have been relatively primitive. They were very targeted in terms of what they were trying to do and what they were able to achieve and I think that's always probably put me in a good place moving forward and Mike. Rare, and I want to think about things like especially my observation is in the whole set of data science. What is one of the challenges with data Sciences with data science as a concept or Advanced analytics oil anything like call it is there's a lot of strong practitioners that can do a lot of great work in this space. But actually I'm creating that link between this commercial thing because of the business and people that actually understand and take things directly to customers or and that link between the data science and then the actual practical application of the data science is not always apparent. When they want two things that we tried to do quite a bit of sports bet is to try get obviously our data scientist on stand and like War but also get our commercial people in our purpose is to understand that link more so they can work better without their science teams. As I think one area that I've been able to add value is that I can crack that lunch. It is a little point of uniqueness that I sort of have going and going my way definitely a very very interesting very good point of unique is what are the things that you have to help with on the two sides that you are able to connect. For the data scientist, whatever and the data people were a couple things so you need to bring to life and help them consider in their work and then same for the business in the commercial people that you're trying to bring in closer to that assigns. I think how can the data science teams understand more what's valuable? So and you do get that assigned to sound seems like quite well, but ultimately data scientist main strength creating a models. I think you need either someone within the data science team or you need someone that's able to tell them. What that is kind of models do we need or it's just it's not so much. What kind of models do you need? It's more what kind of problems do we need to you need to solve and that is a commercial question. Not a date Science question. Once you understand what kind of problems you want to solve then someone that can actually start the conversation around. Well, this is how we can you Miracle solve these problems or this is how we can mathematically do it how we can start to think about it from a modeling point of view. My job probably stops a little bit there in terms of months. We've worked out what kind of models we need to create and based on the problem. We're trying to solve essentially. It's people that a lot smarter than I am. It's their job to actually bring those things to life and to make them happen, but I'm very much focused on the Y and a little bit into the watch and then the data science team can focus a little bit more in the house. That's really good. And when you're when you're faced with a long list of essentially or potential problems, that could be solved through data science in the business. How do you start to prioritize and pick where to put the efforts of the team where to concentrate on it's a good challenge. I mean I notice the way it's full spin them shorts away and many other organizations as well that especially with data science female to the new this literally thousands of things that data science could potentially tackle and thousands of things business either wants to do better or things that the business currently doesn't do that data science would enable them to do so, we need to start putting frames around these things as to what the commercial benefit of each than they actually are ultimately everything comes down to a commercial use case in terms of how much money is going to make think. It's all spent were very focused on customer and it helps simplify a lot of so a lot of it for us is what's going to actually improve the customer experience that's sort of a barometer as to how we look into these things and how we prioritize these things but it's a challenge because with any data science model, I mean some things that you try do I just really hard and you have no idea how long some of these things going to take and sometimes you don't know you can actually be able to do them at all as I say my guys quite often if you're not making mistakes, then you actually probably not pushing the envelope partner especially if they said that most of what you do is ambitious and most of the The data scientists when they start doing it, they probably don't need to know how they're going to do it. They have to actually as they work through these things. They're going to ask you to work through a lot of these problems and work out how they can't do something. That's a cool space to be working on but it's challenging space to be working in as well. It's a challenging space to manage and to organize and to ensure that your team is continuing to deliver results. That's exactly why that's really good. Really really good actually and did you have to provide almost like any education internally to help people? Stand over see people are totally data science team to help people understand that sometimes the problems will take a long time to be solved and then sometimes they can't be fully so or it might be a partial solution. Obviously a lot of people have difficulties explaining and getting people on board the ambiguity around the process of data science and the length of time that it may take to solve something what have been your learnings on that space. I think something that we've worked on quite a lot especially in the last Size six months is around how we communicate ideas how many habits are not so much ideas, but helmet how we communicate the pieces of work that we're working on having to constantly back to stakeholders and customers. But also how we just communicate how data science works as well and delivering things like making people understand the Modern Life Cycle. So one of the things that we did which work really well for us was we actually painted out this is what the Modern Life Cycle s you look like and we presented this to a lot of people and the feedback that I Not from that was that people understood exactly that that tool that our defense help them to understand the process and understand where the difficulties learn the process a lot more and then in terms of the communicating that constantly that's that's something that has always been a challenge. I mean if you're embarking on a project that may take six months may take 12 months you're embarking on those projects you need to bypass to cut them up into bite-size chunks so that you can constantly communicating on them. I think one of the mistakes we probably made when we started doing data science, seriously it Sports It was that we took away those projects and then we saw this quite frequently was that someone would take away their sort, of course the stakeholders the start of the project get their feedback at their requirements and start building the model and then set a set of meeting six months time after they're done all this work and modeling and then that presented back to the stakeholders and the stakeholders would go. Yeah, that's not really what we are wanting and therefore see what work was sort of as debatable as to where we've got some six months worth of work something that we've tried to really address is how do you how do you put these things into bite-sized? How do you get people understanding what the progress of things are what the direction of things are? So we with the concept of product burners for models. So we'll have actually a commercial person or a subject matter expert who is outside of the data science team who can actually work directly with modeler with data scientist to help steer and but also just to help understand what progress has been made and where it's being made so that we avoid that conversation six months down the line when people say that's not exactly what we wanted. That's great this relates to to something. The you said before around bringing the people outside of the data science team getting them to understand data science and how it works and being able to work better with the team. What are the some of the steps that you've taken in that area and getting helping people get started like that I ask because there's a lot of people that are really facing that problem and wanting to increase the data literacy in their organization. What are some ways that you've tackled that problem. Yes. It's an ongoing thing that we're trying to tackle. I think my message So I went quite closely without without product management team here. For example, I think this is definitely the case for a business like sports bit, which is very digital very numerically Focus. We sell digital products essentially the product managers role over the next couple of years will change so that they have to actually know understand how they can utilize it Marcellus and if their product already sent a number of calls of that so it's not really a negotiable that these some of the commercial owners actually understand this better. And that's true of product management is true of true of other areas as well as the modeling. Becomes more and more sort of critical to what we do at the moment probably about 30 or 40 percent of our homepage of our app, which is pretty much all most valuable piece of real estate. We own is pretty much run my models. So in order to understand what we do or understand how our business runs you sort of have to understand the modeling that goes in behind it one of the key sort of areas that I talk to people about in the space Stan how the model Works necessarily how to do a model or had a creative but you do need to understand the why and the what you don't need to understand why the models being As needed and you understand the need to understand what problems being solved back to the point I was making before and like I said it that's actually not so much those questions aren't so much data science questions are actually commercial questions. So if you get people thinking like that is the model is complex as the model is into some people myself included some of the work that the data science team does is just what you can start to get down there artificial intelligence World some people that's incredibly confusing but that doesn't mean that they can't understand and comprehend the reasons will the outputs of what those models are producing and if they can understand that and they can make their decisions and that can ensure that their products are utilized in some of this emerging technology and these emerging thoughts. One of the other things that we've tried to do is try where possible to bring the models to life spent a good chunk of the last set of 12 to 18 months trying to explain to people a lot of these models through PowerPoint presentations Etc and a lot of diagrams of a lot of things but I really good example, which works really well recently. So we have a record Dyson model, which is pretty recommending events that customer men want a bit on a specific point in time and how we do that is like a lot of people do their recommendation systems is through a fundamentally a look-alike model and the look like model then once we assume which customers are similar to other customers, we then assume that whatever those customers are betting on Upon a Time as what the other customers wants to give on it at a particular point in time to explain this one of the things that we did was we got people in two sessions and they came in and we got people to put this in To a music analogy. So we got people to give ratings out of 10 on a number of different popular songs. We did this real-time with a sort of mobile survey. And then once using that information that came back from people we were then able to run a model in real time and then we're able to say okay. These are the people in the room that have the most similar music tastes to you and it was a really good example where people came up to me after those sessions and said I get it now like you can sort of made it more real and savings of I Now understand you've shown me an answer. Real world example in part to life. That was a really good example the work for us but it still remains a work in progress. And like I said, I think the next set of horizon for data science is very much around as scientists can't just be people over in the corner doing cool stuff over there really have to be integral and in the middle of what's being done business, especially a business. I supposed to do what a brilliant way to bring the example to life. I love that. It's such a great idea and it's you've been growing the team within a quickly growing. Organization there must be a lot of challenges in growing the team in structuring the team what are some of the things that you've had to overcome in that space. We fundamentally there's obviously a space of that of talent. There's a lot of businesses that are trying to ramp up their their data science capability and there are there Advanced analytics capability and there's not a huge amount of Supply out there at the stage. There's a lot of people of its relative to the amount of man the supply and demand spaces a little bit out of whack. So we need to make sure that we can Get this town because it's obviously a space where you really smart people and you need the people that are really going to remove the down the space. So we need to make sure we get the right Talent by having cool proposition for them as and cool school stuff for them to work on and I think we had that at sports but we're fortunate in terms of as I mentioned before we're fully digital business. We sell a digital products pretty much nothing that our customers can do or their happens in our business that isn't tracked in some form or in some way and data and so we're very lucky in terms of with swimming and data and and also the uses of that data or the use cases for what we can do in this space a huge and so we're very fortunate because it helps us to get Talent probably keep talents as well in terms of getting them working on school projects. It's definitely a battle and sounds of making sure that you get the right talent and then also making sure that you can keep that Talent on board because a lot of people we get as well. We generally give people a little bit more junior or not so much that but a lot of the stuff that we're trying to do spoken for especially in the Australian Market is quite ambitious and Not many businesses that are probably doing it as though there's very few sort of places that we can go to and say have you done this before and so we're getting people and we're training them up. A lot of their training just happens on the job where they just cutting their teeth on cool projects. We need to make sure that once the got those skills, then we can keep and utilize those skills. So very mindful that in terms of structures. I think that whatever supports and structures you can put around around these around data scientists bear the way I like to look at a data science team is that data science team is interesting in that it's got It's Downstream stakeholders and its Upstream stakeholders. So Downstream stakeholders Data Solutions teams with the data Engineers. The people that both Supply the data that the team's work on but also the teams that deploy the models and push them its production and then you've got the Upstream stakeholders. We've talked about quite a bit in terms of L commercial stakeholders and the people that are actually taking stuff directly to our customers which puts data science team sort of Bank smack in the middle of that process with all due respect to data scientists and I come back to the fact that You get a team of people that are fantastic at creating models data scientists aren't always as a generalization the best set of connectors or the best set of stakeholder managers sit in the middle. So team assist in the middle you need to make sure that they have those strong links out. So what kind of structures and support can you put around these teens, I mentioned the concept of product owners before that work into the to have similar sort of Concepts, although different names from our data Engineers as we also make sure that we put an ailment roles like business analysts and with our data science teams as Well, they're almost like the glue that can help these teams focus on creative and models but also can be there. We're making sure that they're worth on the right things and can have the resources and have the data have tools that they need when they need it. That's really good. So there's that mean that the structure your department would you have teams according to specialization or capability or is it based on the project work and sort of a multidisciplinary team whatever way and what way would you? Do you normally structure that department? So we don't get a buzz that the sort of actual structure of the team's much more sort of my disc 1 and set of people you actually report to but how we run every project is that we try making this cross functional as possible and we find that that works a lot better. So we've had a big push in the last 18 months 20 years on personalization. There was an area that I lit a project that I live one of the biggest challenges with their project was that you're essentially trying now create products that goes across that original. Teams across data science teams and across front of delivery teams for the time that goes onto the platform. That's not to speak about the commercial owners in the product owners. See you're trying to deliver product across the three very distinct groups of teams. That was definitely a sportsman was probably the case that a lot of organizations had never really worked together in the past. It's correct things one of our major learnings was that you can't just have those sort of hand over points across those teams and expected to all just magically especially teams that were unfamiliar with each other some more and more. We went down it's more and more we went. we shaped out sort of structure of the projects much towards project-specific cross-functional teams that incorporated those sort of three areas around engineering their science and front of the Livery then Incorporated those teams as one team within the project and that increased our throughput and increased quality of what was coming through significantly nice and tell me before you mentioned that a lot of the type of work that is being done in your team's is the kind different and in some ways much more advanced than what would be done in the industry in general and that therefore you hire people of them will learn on the job for that reason. What type of things do you look for when you're hiring. How do you determine the potential doing that interview process to see how go there is a person could do in the job one of the things I love for maybe sort of typical of my background and the story that I told around I said, I've got to where I go. I really look for people that are in business and people are real so So I'm not looking for and I probably prize that over a lot of other things around obviously, you need to have a base level technical skills that you can show and display. But ultimately I'm someone that can make something happen and looks at a problem that maybe that others might find difficult to solve. They look at that problem and go why don't know how to solve it today, but I'll work it out. That's a really cool skill to have and really cool. If you've got those people on your team. Nothing is unsolvable and that regards because you can always it can attack any problem. So real sort of stuff so I was not the kind of person that was the kind of person that doesn't need to be told what to do or how to do it necessarily they're going to try work it out for themselves and that's doesn't mean that they're on and trying to work it out themselves, but they're going to really try to look at problems from a number of different angles and work with people in order to solve it just because it may not be really really solvable and I think that plays back into sort of how I got says where I got two of my career. I mean that was that was probably my one still was I was able to I was able to look at problems and actually look at them in a different way and attack them differently and not really sort of even though I didn't have the Skills to do a lot of the stuff I did I still tried to do it anyway, and that's the kind of people that people always say before he can and this is true of any type of analysis or data scientist is the technical skills while they're very important. The technical skills aren't will set you apart because probably there's numerous other people that has just have just a stronger technical skills. But what can set you apart is what you can add on top of those technical skills how you can send that directly into actually solving problems and how you can actually apply unique solutions to difficult problems. That's right. We great one of the things that really jumped out to me about your career is that you have done technical roles or roles that are more of a technical bank that have more of a technical event and then moved to rows that have more of a commercial bent and then back to once with more technical bent and then vice versa. So you've sort of had roles interweave with one another of have different focuses. And obviously, for example, one of those changes happened in your time. I Sports bets going from Analytics to customer operations as a GM and then back to a GM of analytics and insights. So what I need to ask you about that those times where you went from a analytics roll to a commercial roll. What were some of the benefits that you gained on the commercial Roll by having had the analytical roll just before that. Yeah. So is she Point your eyes? So the role as you mentioned looking after a customer questions team, so that's our customers. Service area l phone getting steamed. So an operation where we take this across over the phone and also some of our custom operations areas like forward and some of our other responsible gambling area as well. It was a massive iron of me. So it was a role in in that sort of 20 people which starters was a was a big step and something that I got huge learnings from and secondly it was in areas that I'd never worked in before so I never worked in customer service. I've never worked on a lot of these areas. So it was a big step for the compliment. That role but in terms of what I learned it was fantastic for me. There's a what I was able to learn seeing things from a whole different side of the fence and start as I think that my analytical skills. I think if you have good broad strong analytical skills, it actually sets you up for a number of different areas. I was actually able to bring my own medical skills and step rolling while I tested every being a customer service X, but I was able to look at things from probably slightly differently and some of the people on the team that were customer service. It's but it's or I was able to work with them on a lot. Of things because it's amazing how many things of business especially a business like sports bit screwed digital. It's amazing. How many things come down to sort of numbers at the end of the day and maths that actually gave me really good grounding in terms of being able to pick some the concept stuff and being able to actually not just Concepts up but actually applies sort of different ideas and experience the areas using my analytical skills. And I say that when I said that role for about 18 months to two years and then came back and took on a another analytical role after that. I think it Definitely made me a bit much better analyst in terms of having that wives sort of commercial Browning and understanding and being able to be on the outside of the fence as well and to be able to be closer to the customer. It made me probably definitely made me a better analyst and hopefully also made me a much better leader. But by the virtue of him to have look after 200 people all sort of unique different backgrounds. So it was a tremendous learning opportunity for me. I definitely recommend it for people who might be there might be analyzed to see themselves. As analysts and then might be there in destination or what they want in their career, but I would definitely tries to open towards other sort of avenues even if those Avenues are only for a short period of time because ultimately I think there's other paths might actually make you a much stronger analyst so interesting and such a great point. So if somebody was looking at moving out of a purely analytical role to more of a business role what type of things do you recommend that they look I was fortunate in this regard so just happened and it probably wasn't so much my intention at so that happen for me. I think one thing that people can do maybe this is why it sort of happened for me. Was that even our analyst or a data scientist or whatever you call yourself? I think you've always go set see yourself as someone who is not just crunching numbers, but someone who is actually working for a much enormous a higher purpose, but a much more looking at the end result of what those numbers are actually driving and what they know to be driving. So if you're looking much more or not just for now almost it's not just about corporate giving the numbers and some recommendations to a team. That's actually trying to work out. Well, what are those numbers actually what are the decisions that are actually being made from these nuns and actually pushing through and getting much closer to the commercial teams and I will make you a stronger analyst and I think every analyst you doing that regardless of if they're trying to get a role in the commercial team or not. Well, they're trying to specify their den career. That's ultimately one now. I should be doing and by doing that I think you then put yourself in a position where you essentially are ready to it is Ocean for you to then move into some of those commercial roles and to and to apply the your analytical skills and a slightly different sort of slightly different way. Yeah, that's fantastic. And I do think that into the future we will have more people that have experiences and cases like yours where somebody would strong analytical skills in there and other kind of legal experience will people like that will go into for commercial roles and vice versa and and I would love to see those leaders come up through the ranks. Acts in very similar ways to what you have done and ideally not only in business but you know government and not-for-profit. It's something that I personally would love to see it. That's why I'm so excited that you have done that already. So it's fantastic. It's a it's a challenge like because I've talked a lot about sort of that's one side of the fence in terms of and I spoke about myself personally being much of a sort of all around the Jack of all trades master of none and that that in itself is a skill in it. Actually I think is a very very useful skill. I hope it is at least In terms of being able to try and to see things through the sort of whole process and to see much of the more the bigger picture that flip side of that is you still need and and especially the data science space as Things become more and more complicated. You need people to dedicate D conference as well. So I think we're going to get into this little bit of an economy where businesses people are either going to go down One path or the other. They're either going to go down that will round up a switch is very valuable to have all their going to go down that sort of deep deep past where they actually become very sort of narrow, sir. Of Specialists, which may be narrow, but it's a hugely valuable skill and that's got its own value as well. And I think that's where I don't want to say that and if anyone's listening on this call thinking ice to choose which one I'm going to be at it that's not how people to react to that statement. But I do think that we're getting into this world. That's where the value is around having people that are around us and then having people that can go deep and can understand things to be of the group. Obviously my my skills and said towards the deep. It was pretty much always sets Rusty or rounded but I Is that there's definitely value for both of those areas. It's true. At least in the Australian landscape Sports about is one of the companies that leading in terms of as a digital company and in the use of data and the scale as well, so it would be natural that you would see that the need for that deep specialization before a lot of other companies that are you know, trying to get to that point and I know for example people that work at Facebook and sometimes there is a team of data scientist that for over. Over a year the only work that they do is to try and improve a single model and you know and day in day out week in week out there hacking away at trying to improve a single model that is in place and seeing how they can make the accuracy of the predictions better. And that's in line with that deep specialization that you were talking about. But as at least as Australia is maturing in this space, there is at least at the moment. There's also the need for that generalist a skill set quite a lot and I can totally see. How in the future it is going to that Stark difference between the two types where will become more and more prominent? Yes, that's nice to hear about people from spending all that time on one model every night. I've had similar sort of stories about had a Netflix as well. And that how you see importance there. They recommended models are and how much people spend time. Just trying to get sort of the Instagram out of those models specimen true and tell me for yourself. What type of things are you thinking about our the moment what's capture your curiosity or what type of things are you uh, Trying to solve. What are you spending your mind share on recently. So a couple of things firstly for us in this is specific to us is global. So we Sports it is part of a larger sort of group that involves Petty power and Beth are over and Dublin in the UK also only okay, and we also have a recent set of changes to bidding laws and the u.s. We have a business called fanjul or required a business late last year called fanjul, which is wrapping up extremely quickly. The fast growing very fast growing sort of area. And so what we're trying to do and taking the lead on this list moment is around. How do we get that sort of global scale? And how do we increase our Global Talent pool? And how do we make sure that we haven't just all data science teams that are operating at our own so pace around vacuum. How do we actually realize some synergies and gain leverage from the fact that we do have that Global capability, which is easier said than done. I mean, we all have differences with data structures and Data platforms and Legacy systems. We have non complimentary time zones. And so we're sort of early stages of global and training really starting to understand why it's so difficult, but that's very much. It's very much on my mind. I think for me as well. One thing that's on my mind that sort of ticking away very much Sports bit is how do we start to get to work? We're doing what I would call sort of real I would say excuse my terminologies for this for a second, but I would say that most of our sort of model. Mona has some really cool models and that's really cool things. The most of them are what I've talked to a linear sort of machine learning models. How do we then decided to become a few more use cases that are apparent to us get started to go down the route of where we actually need an artificial intelligence to solve the problem and not just not just if then statements or more linear models. We've actually got the the machines that are actually driving the conclusions. You're not necessarily telling the machines or prescribe them sheets as much as to how to solve some of these problems. It's as that's interesting because it's probably haven't had those use cases on our radar for a while. But this time to be some some interesting use cases that come up. Yeah, that's really good. So we'll play that do you mean moving from our like supervised learning methods where you might give their input data and the answer get the model to extract the rules out of that and moving towards something like reinforcement learning where you have sort of a rewards for the algorithm and it has to work out what a good action to take on its own. This is So you just explain it better than I did then no. No, that's really good. And obviously only if you're able to share but what are some of the things that you're looking more to trying to solve through artificial intelligence that this kind of varies in terms of some of the models that we currently have. So a lot of our models are put into two categories one category is - improve the customer experience directly and most of those personalization models of which the recommended model that I spoke briefly about is one of those and I think that our recommended model is a real challenge. Challenge because little press seconds that compare it to that in the Netflix recommend a model. Where is that? They're trying to recommend the movie someone that's movies going to always old movie or TV program. It's always going to be there for the next set of three to four years. At least we're literally trying to recommend someone a horse race that literally has a five or ten minute being window at people better and all of a sudden once that race starts. It's finished incentives. It's no longer on the site anymore and we have this very very highly seasonal sort of dependency where customers may have their own. No preferences, which is obviously a very deep at specialization in terms of what sports are like spit on but what really overrides that sometimes is the seasonality. So to use an example like an international example a customer may love to bet on the NBA, but if they're coming August when there is no name be a on then they obviously not there to bet on NBA so it's been a real challenge but they'll recommend the models. I think there's a part that some more sort of artificial intelligence can play or reinforcement learning and deep learning that can actually come in to enhance some of those models. Owls and can give better recommendations directly to our customers or another major area that we focus on is oddly sort of termed a sports models. So for well Sports models are the models that create prices says what cut for immense look at the example for an NBA game. I want to be a game we have about four or five hundred different markets that customers could get on so everything from who's going to win the game all the way down to how many point six players going to give or even how many assists or players going to get and so Those are all running through models and we don't have humans that are passing up almost to all those markets. Unfortunately that that would be impossible. So we have models that do that. There is the ability to take that further to utilize artificial intelligence to improve those models even further and we have we have a product called same can multi. I'll briefly explain to you what a savior multi is and why the modeling either this is so-called dating 101 for so a multi-bit for those that aren't aware multi that is essentially where I can take two different bits. I can combine them together and the outcome of that the times those together and that consists price. So let's just say that I had two outcomes that were both 50% chance of happening times in together and for bosons happen. It's 25% chance for them to happen. It's very popular products because customer get the custom make is higher prices and a bit more excitement with their bed as well. The problem with that is that only works for events that are dependent independent of each other. So for example in the same game just Encapsulation so I couldn't say Golden State Warriors to win the game. And also Steph Curry who plays for the Golden State Warriors to support x amount of points because those have been silenced and independent from each other but we've created we've created a model that can actually priced those events and can actually press the accumulation of dependent events. And that's based at the moment on heavy sort of simulation modeling and we were the first we created this model that we really spoke to about two and a half years ago, and we're the with the first business to create this product for our customers. Snuff customers love this product and since then my shoulder competitive have followed pretty quickly. But we want to stay ahead in that game and the second are multi sort of space. So putting more and more markets in terms of things and the complexity of what we want to do and what everyone says some point of time. It's going to get complex enough that we're going to need some sort of go to a new sort of dimensional problem. And then this was artificial intelligence and then there's other things. We've got like a register or a list of things that we want to consider in terms of Of a list of that 20 or 30 items that we think that we could attack was some artificial intelligence and things and some Basics that are text analysis or machines that basic but some text analysis or some sort of image detection work or even videos tection work that can help our customers as well. And once you get into obviously image detection or video video identification, then you really need to start to get down a world where you're doing much more AI than sort of a linear linear models. That's sort of Hands up some of my thinking on this bus that's fantastic and tell me what excites you about this field. Everybody thought of that. I think it's just intrinsic in terms of I have a sort of and probably quite cheeky passion towards using numbers in order to get to an outcome, especially in what I do today with sports betting the United absorb a sound while I might have failed nests at school. I was always big on so Sports statistics and being a mess with sports fan and I went to bury here. These are sort of numbers. Side of sports was being passionate for the way you were able to break up something. That's so complex like sports and you are able to put numbers into to make sense of it through ultimately and I probably didn't think of it this way of the time but ultimately through mass and you're able to improve things so things like the Moneyball examples for those that are familiar with either Mabel movie or probably more importantly the box that Michael Lewis wrote around money for terms of the way to teams are using Sports and alerts to improve their own field the way they play the games on field and the way that I choose the teams and all those sort of advantages. I think that's fascinating how it pays and utilize numbers in order to make things happen in the real world. I just applied it to what I what I do day to day. I think there's difference do find it fascinating how you can make improvements through breaking things down into sort of numerical or mathematical Concepts. Fantastic. I've got a tough one for you, which is how has a failure or an apparent failure of the time helped you in your career further down the line one. Things I can think of is your like something I was talking about before was when we first started to do personalization as supposed to be I mention that we've started that probably about two years ago. We first created a model which was based on customer preference thought we can create a kind of model here that tells us what Sports customer likes to bet on and if we display that directly to our customers we fantastic that customers will click through on it and use it all the time and we quickly learned that wasn't the case and it took a lot of work to it looks killer Works obviously productionize that model and About we put it on our friend hands and it was a model that was running in real time and talking about something that runs offers true one-to-one personalizations customers and has to run the model in the Split Second between the customer clicking on the app and the app loading and the homepage splane to them and so really cool piece of tech that we had our Tech teams working on for a long period of time, but ultimately it did nothing for our customers. I mean the reason why after instead of interrogating into it was because of what I was talking about for that that time and since it out set seasonality is Is just as important if not more important than the customers preference. It was a massive learning for us and something that we were able to then take away that helps improve as we even further on the personalization Journey helped improve what we were doing significantly and have this sort of going to hold from paths as to how we approach the personalization of a personal point of view. Obviously, it helps me to steer that Direction having that sort of insight but it took was pretty painful sort of experience to go through all that but it goes back to my whole sort of Point around if you're not making mistakes, then you're pushing the envelope. And so fortunate enough that the business was able to sort of ride that out and state stick by us through that process and we've obviously come out the other end much better. It was a good learning for us. It was a great example of customers telling us or in this case. Probably not even telling us because they really Bunch from having this having this personalization available, but customers telling us what they actually want and therefore being able to Pivot based on that. That's her great lesson. I didn't that case. Was it about the cash? Chalmers wanting to have the choice and being able to browse themselves, or was it more about the decision ality as you mentioned. What do you think were some of the reasons behind it now? I think it's mainly the seasonality to play around and we've done the sexually and testing subsequent. We've actually tested models which pure customer preference models versus character versus models, which are pure seasonality models in terms of no account customer preference just have a cat for what's trending in a particular point of time. Obviously. This is more specific to our industry, but probably could taking things further. All Industries the seasonality only models outperform of the customer preference models and out an hour ago. It's a good learning that we've been up take away that obviously we can get both of them to mind. That's when we get the best recommendations for our customers. But we do need to take those into account often wondered with businesses like Spotify or whatever where music like how seasonal is music and buy your own sort of listening listening habits in terms of this songs that sometimes you love to hear that song and sometimes you wouldn't mind together at some and so so I wonder where a company like Spotify how they deal with us, but that seasonality which doses of the good thing in our Industries. The seasonality is very apparent but it's quite obvious. Whereas I don't know how I'll be would be something like music preferences or food preferences Etc. Okay a great example. Yeah true. Wow, that's really interesting and in the industry no industry. There's this thing called The Imposter syndrome, which is when people who say data scientists who know how much I don't know in this space. So they recognize that it's a big field that it's impossible for one person to know all of it. But sometimes by realizing how much you don't know that makes people feel almost like an imposter in the sense that they think of themselves, you know, as a proper data scientist ordered as a true data scientist, and I know that in my career, I've definitely felt that in the past. Have you seen this through your career? And what are your thoughts and perspectives on the Imposter syndrome? I mean obviously from personal. I love you, Iris Lee had a lot of the Imposter syndrome in terms of I lead teams with people that are much smaller and much they're doing what they do in the data science space than I am. But then I am so happy place that day today, but in terms of what probably concerns me, I think about data scientist is where I've seen the not have imposter syndrome and to the point making their I think not having imposter syndrome is almost it's actually a sign of lack of knowledge. So I'm a big believer that the more knowledge you accumulate about something the more you realize As you don't know what day more things there are that you realize that you don't know so generally learning curve goes the more that you know, the more reason to re-up to start to think that you don't actually have all the answers. And so I think I have seen this in examples seven people especially in the data science by someone that is the more arrogant about their skills or isn't willing to actually set that often as a sign that there's so many other things out there that they're just not across or that don't know so in some regards I would actually probably we encourage people to have imposter syndrome and encourage people to actually face into the fact that they don't know a lot of things specially and data science space because basically no one knows everything because it's such a huge emerging and complicated field where you can sort of reduce your arrogance and throw your hands up sake guys. I don't understand this and can work with people the more you will learn because that's what's important to my points before around the skills that I look for. A better scientist is very much around that ambition that self-starter and that person that can tackle problems that they don't know the key. The thing that they were they need for this learning and so another way of putting that is that a data science is key skill they actually have is need to have is the ability to learn so I don't know if that squarely answers your question, but I think maybe the flip side of your question but like I would not worry about the Imposter syndrome at all. I would almost worried the opposite. Yes. So if you have it, it's a good thing. If you don't then maybe reconsider, that's really great Tony. This has been an absolute blast. I only have one last question. For you and that is what is a takeaway or a parting thought that you would like to leave the listeners with something for them to consider as they go through their career. Okay, put me on the spot here. The was my one word wisdom. A lot of what I've said is spoken about this sort of Need for ambition. And I think anyone that's in the space. I think they should realize they're in a really really exciting and growing space. I'll sort of talks about why I still it's exciting that someone as I sort of didn't mention was that this is Because it's going to change so rapidly in the next five years. I mean even changes changes in a period shorter than that and that's exciting. It's Louis scary. But overall it's exciting. Its people need to be able to embrace that change and people to the other points. I would make they just need to be able to learn and adapt their skills through that. I mean ultimately we can solve some cold some awesome things. There's so many examples out there where businesses have been able to solve solve fascinating problems through the use of data science and solve some of society's biggest problems through the use of data science or Advanced analytics. I mean one more examples. I have a mission for sports bit, which I'm probably most proud of there's a recent model that we've created that can do a very good job of identifying people who we sent may have problem gambling or may have shown signs of problems in their gambling behaviors and habits and we can utilize that model to proactively identify issues before they become issues. Obviously, very proud of that from us. It's a great example where we were actually able to help improve Society through Use of data science is plenty of other examples that you can think of that the world around examples where flu breakouts have been able to be curtailed through the use of data science or things like the ability to handle crisis situations through this in something of about using tweets and crisis situations in order to understand information include information a lot easier and to understand where the root problems of things are. So there's some really awesome things that we can do. Do and to really sort of strong some awesome intellectual challenges at the risk of being a little bit to sort of altruistic about it and little bit so potentially optimistic the data scientists of Tomorrow are the ones that are going to actually solve some of the world's biggest problems that's called be a part of and I think that anyone that's probably listen to this call or that's trying to be a data scientist or wants to make strides in this area. That should think about bless you have their emissions being their high but they can actually change the world because was and improve the world was Skills because the ability to take that data and turn that into something meaningful and turn that into sort of progress as a bit of a superhero skill, and so you should view your skills, and that way I could not agree more that is outstanding and a fantastic note to end on Tony. Thank you so much for your time for sharing your journey your insights your wisdom. It's been an absolute blast and an absolute pleasure. Thanks so much again. Thank you so much corruption in Florida. That brings this episode to conclusion. Thank you so much for listening. Please find us on data futurology.com or on Facebook Twitter LinkedIn or Instagram as that of your Trilogy. Also go to data futurology.com forward slash podcast to find the show notes for this and any other episodes. If you liked this episode, it would mean a lot to us. If you could leave us a review wherever you listen to our podcast. I hope you enjoyed this episode and that it was helpful and valuable for you. Thanks again and you next time.
Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016. In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams. Enjoy the show! We speak about:   [01:20] How Tony got started in data  [08:20] Tony’s skills come from the commercial side  [11:10] Linking data science and the business  [14:30] Communicating how data science works  [17:00] Steps to getting others to understand data science  [20:40] Getting the best talent for your team   [24:00] Structuring teams and the department   [28:10] Transiting from analytical roles to commercial roles    [35:30] Working on global expansion   [38:10] Solving with artificial intelligence   [42:30] Passionate about using numbers to reach an outcome   [44:00] Modelling failures with Sportsbet    [47:50] Imposter syndrome in data science     [50:05] Data science is rapidly changing and exciting   Resources: Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/ Sportsbet: https://www.sportsbet.com.au Tony’s Twitter: https://twitter.com/gruebz?lang=en  Quotes:    “There is no one path that always works.”   “There are literally thousands of things data scientists couldn’t potentially tackle in any business.”   “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.”   “Not having imposter syndrome is a sign of lack of knowledge.”   Now you can support Data Futurology on Patreon! https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! Thank you so much for listening. Enjoy the show! 