Hi and welcome to data futurology the podcast where we hear the stories of data science leaders working in the field today, and they tell us their Journeys their mistakes that Lessons Learned so you can benefit from their experience in your career. My name is Felipe Flores. I am your host. Thank you so much for tuning in. I hope that you're having an excellent. Excellent week today we're speaking with dr. Breeland kiss Hilton blind is based in. Cambridge Massachusetts in the u.s. He is an extremely accomplished that assigns leader. He will tell us a bit about his journey, but he started as an astrophysicist from the University of California. He then went on to Harvard University where he was teaching and then became head teaching fellow for data science at Harvard. He's worked with NASA. He's worked at the harvard-smithsonian center for astrophysics. He's worked. At MIT he started an international Consortium for Science and Technology doing industry research and development in data science. He's been head of deep learning at a large Healthcare company and now he's working on his own startup, which is currently in stealth mode. But obviously as you can see he is an extremely extremely accomplished data science leader. He thought a lot about what good leadership in that a science looks like how that differs from startups to large corporations. There's a lot of work and Advising in that space. I hope you enjoy the episode. If you do please share it with a friend. I'll be forever grateful. Thank you so much. Here is the episode with bullet. Hi, this is Felipe today. I'm speaking with Poland Poland. Thank you so much for coming on the show. How are you doing? Thank you. It's a pleasure Philippe. How are you? Very very good. Very excited to have you on the show. Thank you so much at the beginning. I always like to ask the guests. About their background their professional background the journey. How's that look like for you? I have been trained as a physicist astronomer. I have been an astrophysicist at Harvard and spent some time at MIT as well worked for institutes that belonged to NASA for a while. So I've been in the space technology astronomy / physics space for most of my career and during that journey. I was very intimately interested in extracting information from Placated datasets which is essentially what we do in astronomy and then based on the domain knowledge that we have we like to see what type of science we can extract from that information early in my career during grad school. I worked with Pioneer statisticians that were applied statistics ins. They did Bayesian statistics and during my time at the Space Telescope Science Institute in Baltimore. I worked what is essentially what's a data scientist today. So I was working intimately. Data extract information making that process Pipeline and working very closely with Hubble Space Telescope science data and then kind of optimizing the engineering process that is primarily data-driven later on during my time at Harvard. I was very lucky to get involved with machine learning some of the pioneers of the field and worked closely with them to implement some of the Technologies and methods that they have developed in that space particularly deep learning. I'm into astronomy and through that interaction with had several breakthroughs that we published in prestigious science journals such as nature. And as that interest was moving forward. I also had initiated a Consortium that operated in between Academia industry and government and that initiative had grown to a considerable number of scientists and engineers and we got funding from some government agencies and Foundations as well and Industry. Mindset was very different government-operated somewhere in between but that's moving quickly aspect of the industry where you can apply your expertise in multiple domains and AI is one of the few domains that I would consider in terms of methodology is domain agnostic. And then once you partner with domain experts, you can make a real impact in a very short time. It detracted me even further and I made a transition to the industry since then. I worked very closely with Healthcare. Annie's Finance in the finance domain as well as some other space technology satellite technology type of companies that develop this that sort of technology. I have been advising Fortune 500 companies as well as startups on the execute of site on how to build a data science strategy how to build a team how to make the maximum business impact in a short time while you build a strategy that will sustain the value d h AI is Delivering in the midterm and long-term. I think that those are very critical questions and also in the startup space how one can find the Right leader for the job and I always say there's no one answer to those questions and depending on the business objectives the timelines to resources the talent pool that they have access to the strategy has to be customized. So if somebody is talking about a single strategy that will maximize your business. Business impact it's more likely that approach will not work for you. That is very true. I agree. That's excellent. So definitely have heaps of questions for you around that before we jump into it though. I wanted to ask you after being formally trained as an astrophysicist and doing so much work that was data science, but before coming into I guess the data science field because you were doing it under the astrophysicists field. We you surprised at how relevant those skills were. And how transferable they were into the business world. How was that transition for you? Yes, or no. I had been interacting with the industry and with government agencies for very long time since before my graduate school years. So I was intimately aware that the basic processes and procedures that you can use to extract information from data are very similar, but the culture I think is primary difference. And hurdle while making that transition there are also challenges on the business side and the people who have been in the industry for so long. Typically they are hesitant to work with people who have been in Academia and the field of astronomy or biostatistics or some other fields thinking that in their own operations. They need a veteran that has been in the industry for 15 years. I sometimes see job descriptions and minimum requirement which says 15 years in Through experience 15 or 10 years data science experience. I'll be very honest. I think that's an oxymoron that doesn't exist. You cannot have a person that has been in the industry for 15 years and have a legitimate data science experience for 10 years that AI data science for machine learning is intimately linked to Academia culturally and from the perspective of know-how. So people who have really the expertise in data science. It's should have a link or some type of semi academic background and I don't mean that everybody has to have a PhD, but if you have been in the industry for 15 years and if you have an NBA coming from different background not engineering essentially, I don't think that you would be considered an kind of a person who has some sort of data science expertise. You might be fluent with the jargon you might have an understanding of the graphs, but in order for a leader to come up with a strategy that will not only deliver the Term, but will deliver a strategy and they he can execute in a midterm and long-term. I think some sort of legitimate data science background or quantitative background is necessary. So I was surprised by that particular aspect and culture wise the mindset of the Business Leaders and from my perspective. There were a few surprises certainly there is a resistance on their part to change their decision making process and from my perspective. There was some cultural aspects that It took time to get used to of course in I love your party from before when you said that the data science leaders do need that the technical understanding that taking Pro background. Why do you think that that is why is that the case? Let's start with the internal Dynamics. Let's say you are a leader, right you want to drive the decisions and you'll want to have informative interaction with your team members and typically in data science operations. Let's say you have one or two. He's that are driving a formal technical progress within the team. And when you have an interaction with that person you want to have an informative interaction with that person. If you are in execute of leader, if you're making decisions and is let's assume that you are in an average later. I mean, we have leaders that are very good listeners. They really listen hard to what the technical leaders are telling them. But this is in practice not always the case. So if you have hard time understanding the ins and Lots of different models and you cannot really have that discussion with the technical leader. It will create an environment that doesn't necessarily make the technical team very happy. So from a retention perspective, it will create a problem in the future. We've seen this over and over and you know when I say we've seen it I I mean I have been working very closely with tens of Fortune 500 companies and what we've seen is retention is a big cost and once you hire the right Talent assuming that you have the right Talent making sure that they're happy and they like what they do. They have a meaningful interaction with the leadership is critically important in retention. Just even from that perspective having a leader that can have that conversation that can command respect within its team within his team or her team is I think important that companies moving forward and the second part is you want to have a leader that has his hands or her hand in the latest of data science or machine learning. So it requires that person to really read some papers. Purrs, at least read some technical reports and have a above the average understanding in order to draw some conclusions and advise the sea suit or the leadership of that company so that the company can build a strategy that can continue to produce the value the business impact that AI has been presumably producing for them. And unless you have a person that has that Vision that has Network within the academic circles or their R&D circles that are Producing that wave of AI information. It is really impossible to just look at the popular magazines or some websites and you know, the general Trends and pain debate and creating business strategy based on what's already popular. So what has been already popular means that this is already beyond the AI wave and if you want to surf today I wave you have to really look forward in an order for a leader to look forward that person really has to have a network kind of intellectual relationship to where that know how he's being produced and I don't see that happening for a person who has been in the industry for 10-15 years and basically is really busy with managing a large team and writing reports and preparing PowerPoint presentations. I completely agree. I love the way that you described it. I think it's spot-on. I'll ask you about perspectives from the other side then by other side. I mean the organization what are the trade-offs that the organization would be making in their decision? When appointing a leader of data science, the reason why I ask you that is because a lot of times when I've seen people getting too into leadership positions that they don't come from a technical background or but they don't have a strong academic background. Sometimes you organization feels like they can speak business. They can speak the language or business or they can get things done quickly or when as you said like those people might coming with the jargon and they might read a blog post. So what they in dine elsewhere and then suddenly we that becomes the strategy that they're implementing in their organization. What do you see as the trade-offs that the organization is making during that decision when appointing that later when I gave my previous perspective when I share that with you, I by no means mean that a person fresh out of Academia would make a great business leader. There are so much that academics should learn I'm I'm talking about an average a person coming out of Academia. There's your so much that would keep we should learn in terms of leadership soft. Stakeholder Dynamics, the business mindset is somewhat different. So one requires. I don't mean to an exceptional or I don't want to use the overused word unicorn but you have to look at what your business priorities are. What type of resources you have and how quickly you want to deliver what we hear very often is that when the company's charted into this territory of AI and analytics in many of the operations have failed and it created a Justified skepticism in business. That AI or analytics is overhyped there is truth to that. But the reason why analytics AI or data science has failed in the early implementations is in my opinion and in my experience out of that relationship that I had over many years with bigger companies Fortune 500 and startups is the wrong choice in leadership. They obviously had very limited resources. The number of people that they could reach out to for a very limited and they basically appointed people who have been executed. You have sinned business for very long time and day thought you know, this is like any other vertical so why don't you lead the data science operations and for them data science meant looking into an Excel sheet and try to optimize one of the columns that they see in front of them. And if you approach a i data science in that limited fashion, I don't mean to belittle that approach. I mean those things are very relevant. I mean the majority of the analytics work that we see in the industry is very similar to just what I mentioned, but if you limit yourself to that Active you will go after inherently after the low-hanging fruit and you'll miss on building a strategy that will build resilience internally for the midterm and long-term indeed. They will feel they're destined to fail and also building a culture that is conducive to a data science operations require some meticulous work as well. You cannot just think of a regular hierarchical company hire top and data scientists and expect them to be happy in the long term in that type of operation is it with You know, it doesn't work. The whole journey of digital transformation is not complete without a cultural transformation. And this is very particular for AI and data science, but I think right now it's affecting overall the company cultures in many big companies and definitely this is the case with smaller companies. So from their perspective, I totally understand why they didn't essentially at first go after academics unless you are Facebook or your Google you can afford to have an RD armed. That you know Burns 90% of their projects, but there is something coming out of ten percent of the projects many companies cannot afford that so they went after the people who have been in the business for very long, but right now we are at a time where we have a Unique Individuals that have a very strong reputable academic Roots. They are known in that world. They have a credible background. They can call themselves analytic experts. I don't mean a superstar, but at least they can talk to you about The p-value what that means and ins and outs and you know, not only talk about regular statistics, but can really dive deep into information Theory and some other deep aspects of the foundations of data science. But also those that there are as few as they are there are people with that sort of background who have spent considerable time and accumulated business experience over time and have the soft skills that is required to transform a culture. So the typical data science leader or analytics leaders until now they have been considered a kind of a cross-section between business experience and domain expertise and some understanding of data science. I'm adding a fourth dimension to that data science leader has to be a person that can transform cultures unless you have a culture Builder and that is required of that real leadership. I think you'll have just in analytics manager but not a leader. There is a very distinct difference between a manager and leader in this context. So you want Have a person that can slowly transform that culture internally make it more so that there is kind of a cultural relationship to either academic institutions or the academic progress that's been made in that context and transfer that know how internally but also when you're tapping into the talent pool that are really good data scientist, all of them have one way or the other have been in an academic setting and those people are typically used to coming up with creative ideas. And if you Overworked those people a hundred and fifty percent and there's no room for them to invest into their continual training and they cannot really come up with creative ideas. And it's very difficult to move forward on the business side again, when they are hiring not only beaters by data scientist based on their skill set. I think that's also the wrong route to take I'ma skill sets can become obsolete in five to six months, even at the mid-level data science including the leadership. What is really important in my opinion more? Then the skill set is the aspect that the person is a continual learner and a curious person in the world of data science. I think Sarge changing on a weekly basis. I mean what is relevant yesterday might be very different may be irrelevant today the leadership the culture and the individuals have to be quick Learners very agile and they have to keep that Curiosity going forward and how do you do that environment for you work 8 to 9 hours a day and that's in an optimistic. Mission and you have partly an attempt to invest in yourself into your training. That's I think disaster that will happen that's disaster kind of a bomb in the making so those type of operations we've seen over and over fail maybe not in the short term but definitely midterm and long-term and the reason why they don't feel right away is is because there are so many low hanging fruit that the managers I would call them that are taking over that operations. They can deliver numbers in the short term because data science has so much to offer but um, Malaysian or automation is not the equal to data science is to of the things that data science can do really well, but there are so much that data science has to offer for company's operation that is very true. So just continuing that line of thought besides the optimization and automation. Well as some of the other things that you see that better science can give organizations. Well, I mean internal agility can help the recruiters I think recruitment is an area that we more often than not. Not not addressing because with the standard approach to see be scanning. I would consider the majority of the real talent is being filtered out. If you go after keywords, if you go for regular resumes that have been coming within the industry you'll miss out on the real talent and for that in order to really tap into the talent that you want to go after continual training of the HR person on recruiters is really important, but also you can use some data science tools to help. Them out and I've seen in some companies where they have come up with smart tools to help them out for that transition. That's that's one area in other areas internal automation. Obviously, we talked about that but data science is very powerful and extracting information. Now, I know that the many companies are not interested in causal connections. They are looking in correlations. But right now we have come to a point where even causal relationships can be deduced from data and that requires the data science operations to partner closely with domain experts but also a kind of a diverse team can bring in the know-how and become creative with data science. I will give you one example predicting flu how that disease propagates with in Continental us or anywhere else. He's one of the most critical questions in the healthcare space at large. I mean you want to predict early in the season how a flu will propagate within a landmass and For each particular ZIP code or state when the flu season will Peak is critically important and this has been done with various approaches. Some of them is public information. Some of them is not but my team has created an approach in which we use some Physics modeling and some Atomic modeling that we used in astrophysics for for many decades. It's called the diffusion equation and what that means is what we have done for many decades and understood. Well within the Domain of physics and astrophysics is we have a fairly good understanding how molecules and atoms behave and how they propagate information and certain dynamical features. And what we thought is within the context of flu what happens if we were to think of people like individual molecules or atoms that are interact with each other and flew as we know it is propagating by proximity. So you have to either touch another person or you have to touch the same thing. So share the same proximity. Person, so can you build a model where you use that information make that approximation about population Dynamics and Implement some other publicly available information in addition to the proprietary data you have can you increase the predictive power of your models? And the answer to that is exactly yes, so you can do so much more when you create a diverse background with creative ideas and public data together. I think that there is a lot of business value. You that can be delivered. But in addition to the business value, you can also save hundreds of lives, which we've seen what that sort of modeling where you can really touch people's lives individual. Especially the more vulnerable ones the elderly and the children that are very vulnerable to flu. There is Meaningful impact that can be made with data science. Yes. Definitely what I've seen and obviously tell me if you agree or have you seen differently, but what I've seen is that a lot of people coming in to data science now that there's obviously so much demand. Brooke coming from from different backgrounds. A lot of them are doing online courses. They're learning about machine learning but particularly on the study of correlations as you were saying, so they're able to identify correlations very well and use those for business. But what I've this would have what I've seen is that the causal inference and the causal studies generally come from different backgrounds that at least, you know, it's like an Australian it's different. Yeah, I couldn't make and I've seen people like from econometrics. Being quite good and that type of study and obviously in astrophysics also good. That's why I was I was wondering whether there was other academic ones that had a strong. Yeah. So let me elaborate a little on the causal inference. Then what you potentially are referring to use not exactly potentially what I'm referring to so until very recently a groups data scientists that have been presumably addressing causal relationship in addition to correlations. Use domain expertise to make those judgments. So right now it's a very Niche academic research based on actual two or three individuals that have math backgrounds or engineering backgrounds into academic circles. Only that have been spending many years on how numerically one can go after causal inference and I don't think I have yet to see in Industry application for that research because it's still in the stage where academics are writing theoretical. Papers on it. So that's the causal inference part that I'm referring to. Whereas potentially you when you're referring to people come from economic tricks. They're looking for causal inferences. I don't think that they're refusing any of that methodology that I am trying to refer to but you still obviously there are many groups many data scientists that are trying to address that exact question whether what they see is closely related or is just a correlation and you can make certain assessments based on the domain expertise you have but you cannot Make an assessment just Based on data only until now I've is again I'm yet to see a data science group in the industry who has the capability of doing so but we're at The Verge were methodologies are being developed as we speak that can do that even though you may not have the relevant domain expertise. Just looking at the data you may be able to tell in the near future whether what the pattern you see is based on causal relationship or whether it's just a correlation. Yes, that would be great. I wanted. To ask you a bit more about the culture side. And so you describe to us the culture that should be creating in data science teams where people should have the time to be learning and staying up-to-date and coming up with innovative ideas. Do you have any recommendations around processes or continual maybe ceremonies or things like that that people can Implement in their team's when I say in a weekly or monthly basis, what are some of the things that they can do to start to create this culture well, I think I have to be Crystal Clear death culture is not only one single culture. It will depend on the culture. You already have the size of the company the priorities of the company. So when I say I don't mean you have to bring in academic culture business operations. I mean that's just overly optimistic and it won't work. But what you want to do in especially a bigger business environment is assuming that you have some of the resources to invest into the midterm. Development of your individual team members and you don't have a pressing deadline tomorrow. You want to make sure that special people who are interested in the data science group is to keep up-to-date with a literature or with some of the new technologies that are coming out every day. So what we've done in the past is we had Journal clubs we had discussion groups. We were asking individual members of the data science group to come up with their own ideas that can be implemented into the day-to-day business operations. With chosen one two or three of the best ideas into our business operations and what we've seen in return is we've created interesting business outcomes out of those ideas that were not in our on our Horizon. We didn't have those ideas. So by having individual members eat as Junior as they are and you know, let me tell you some of the most interesting ideas comes from the most Junior people that are coming from a very different background. So this is one of the reasons why the diverse team is critically important not only E4 culture sick, but from a business perspective. It's really justifiable. If not necessary to have a diverse team. So having individuals make presentations on their crazy ideas and then come up with one or two of them and you put it into your operations is some risk taking but if you diversify that risk, I think there's a lot of value in that sort of an approach to managing data science teams, very true. And in your work with large companies like the Fortune 500 Great, and then with startups what are the differences that you see in the implementation of these types of ideas between the two groups and various other things should be yeah, could you tell us a bit more about that? Sure typically larger companies. They have their resources that they can use to buffer any sort of risk that they have in their operations. Whereas in startups you want to make that impact yesterday. So when you are in a start-up environment the type of talent you are hiring both for the individual members and Leadership can be and probably should be very different as opposed to the talent pool that you are trying to tap into in a bigger Enterprise and it that will really depend on the leadership the culture that they already have and I particularly am focused on the bigger companies now if you have the vision and if you're empowered by the senior leadership, I think you have more of a leeway to take some more risk and invest for the midterm and long-term rather than Just go after the low-hanging fruit but by doing so you have to continue to Justify Your Existence to the investors of your company. So you cannot just say, you know, I'm trying to build a team that has this vision for midterm and long-term. It doesn't work that way in the industry. So you have to be also Vigilant and smart about what you're delivering today in order to Justify Your Existence tomorrow. That's right and within that what are some considerations that leaders should have when choosing what to focus on and what to focus on in the early days versus it of one or two years after the team has been established there. I'm a scout the definitely face this challenge in the past where when I was building what ended up being about a 50% team in a large organization. I remember that at the beginning in the at least in the division. There was no concept of data science and what it could do and one of the challenges then is what do you do first in order to prove the value? Lou so then you can get more resources but also have to build a more strategic plans that will that will give you benefits in the medium and long term in your opinion. What are some things to consider when balancing those things? Yeah. That's a tough one to answer because it will change Case by case again, the business objectives to operational priorities. What are they? And where is the company at that current stage and time what type of pressures are you encountering from the board or Or whoever you're interacting with but typically, you know in all companies irrespective of the size. There are operational bottlenecks one can optimize directly intro value in the very short term. And those are the things that one can go after and then identify internal bottlenecks operationally, but also in terms of the business that they're trying to conduct if they're in Insurance, how can you increase profitability? How can you increase members? Of which in return directly will affect the cost of the insurance company for instance. How can you go about those and you can prioritize the difficulties and the potential Revenue that would come with a particular problem and go after them strategically very interesting very true. That's really great approach. I can tell you that's not the approach. I took invites. I'm unfortunately where it has not been there first. I have taken in my earlier Ventures as well. Yeah. Well, that's that's Sy the voicings of experience and your reviews now are fantastic I had come from I did about 12 years of Consulting before working in housing financial services and and I started a team at a bank is about six years ago before my current job and because I came from Consulting in the first six months that I was at the bank trying to build this data science team I did lots of prototypes lots of plc's for different business heads different Executives, and it was something like 50 prototypes in Months or something like that and it was just throwing stuff out there to see what would stick and then what happened was that nothing stuck? Like literally nothing really worked. It didn't get the interest of the organization and I was getting increasingly frustrated and I didn't have that proper view that you know, why yes tell me so what we see over and over in this cultural transformation processes, unless you have leadership that shares the vision with you. You cannot build that culture. Culture a bottom-up. It's never going to work. So if you're coming in as a data science leader, but you don't have an ally or a person that already Champions your ideas within the higher-ups. I would tell you it will fail. So when people are you know asking me what what should we do? I mean, we have a board or a leadership that doesn't believe in what we're doing there in this only because there's a - they don't know what to do with it. I say good luck. You really have to patiently either educate them. And tell them you know, what will happen. If they don't ride that a i surf appropriately they will fail in the midterm and long-term. So sometimes it does relationships a little bit of Scare Tactics might work and that is a Justified approach. I think especially for people who have been in the business for ten plus years. They have been sitting in their bubble as well as people have been in Academia for many years to have been in a bubble. So breaking that bubble getting another setting of The Other Side is critically important and I think people in the business they have to To respect the fact that AI is a very much intertwined with things that are happening in Academia. Typically, The View and perspective of Academia and the business has been not the most positive one in the past and AI is not a product that they can buy or implements like a Lego into their day-to-day business it will fail and it has been failing. So either those business leader get their act together if they want to survive this era of AI or they will Obsolete tomorrow. I mean that's as simple as that and if they don't get their act together, like some of the executive some of the higher the most prominent Executives in the industry. They require all their leadership to be a i literate or data science literate and what it means is they have to be sit down and code and that's the first step. So if you don't know how to code it means that you don't know the alphabet of data science and without that you can memorize Shakespeare, but if you don't know the alphabet, you're not going to be able to come up with a new new paragraph right in literature. You cannot claim to be a disrupter. You can be just trying to keep up with the AI wave and claim that you're producing something that gives interesting results. Any person that has a Keen Eye on what's happening in the data Science World. It will take them five to ten minutes to see beyond the smokescreen that companies have been creating so true. Yes, and I've definitely worked in in Oregon in the past. I worked in organizations where they felt I know that it's changing now, but at the time they Like they couldn't buy Innovation and their words their minds. He was just like okay how much money do we need to dish out and we'll work with startups and we're work with small to medium businesses and will partner with other big organizations and well, yeah, our team's most yes and just spend until the Innovation shows up. I understand that business perspective. Let me tell you that. I mean it's one thing that X academics can you really learn from business people and there's so much that we can learn from them, but in return they have to really respect the technical expertise and The whole package as it comes it's not just another vertical. It's going to be the engine of the future companies. I can tell you this might be a little speculative but all companies will become AI companies one way or the other. So if you want to be relevant for that era, the culture has to change and you see that cultural transformation effort already in place in many Fortune 500 companies in the financial sector some domains are doing better than the others but that cultural transformation I think is T in making true that digital transformation reaches its potential very true. So interesting and tell me what the moment what types of challenges and problems. Are you currently thinking about in grappling with some of them? I cannot tell you I have been I have been very much involved in advising Fortune 500 companies and also helping out startups to get their data science effort soft around round building and appropriate team that is customized for their business objectives and timeline and operational bottlenecks and I have been thinking a lot about the leadership profile that companies should hire and there have been challenges in the past and it's very unfortunate to see that many companies are still going after the wrong profile that are destined to fail in the midterm. And when those quote-unquote leaders joined their company and fail in the midterm, they will just leave there. Host go to their next job with that experience whereas the companies will have wasted their resources and time and Dai wave will be unreachable for them. So it's critically important for them to get the right profile in the leadership positions and they have to decide whether they have they want to ship the AI and data science operations under a CTO or whether they want to create a sea level position for the analytics or data science operations, even that decision by itself. Elf shows the commitment and vision of the board and the company. So those things are critically important and I must say, you know that there's not much time left for those companies to catch up and ride it as AI wave if they you know, sit in their corners and you know try to come up with interesting strategies and are asking people who don't who are not domain experts to give them ideas about strategies if you don't cross check with From people coming from different domains. It doesn't make sense to me and I don't know how it makes sense to them. Yes, it's true. And we spent some time talking about the data science in large companies. How does that data science leader change when it just start up and are there differences when it's a start-up in general versus in high startup. What are your thoughts there? Yeah in the startup space. Typically the hierarchy is the titles become somewhat blurry and irrelevant because you have very limited resources and you Everybody to chip in at their maximum. So you want to hire a person that can pull up the sleeves and get your hands dirty. Whereas, you know, if you are in a larger company setting a person that has that expertise can roll up the sleeves at sit down and code with you definitely commands respect definite can create the climate and cultural transformation for that company, but it is not always required in bigger companies. I mean sometimes especially if you're managing 50 60 or more people it will become a very Difficult. I personally try to keep myself very up to date and keep my hands dirty. And I typically I keep my scientist title in addition to my executor of leadership title, which means I give a clear message to my team and to company that I am a Scientist first and then a leader second and you mean in my in my opinion my style without having an intimate understanding of the processes. It will become somewhat difficult. To be that leader that I'm trying to be so in larger company settings for skill sets or areas that one can focus and their weights can be a little different as opposed to the skill set that you require from a start-up leader also from a personality perspective. They can be very different from my personality as well. Absolutely. I mean you want to have a very curious Dynamic person all leadership positions obviously, but this is especially true. True for data Science Leadership. I mean unless you are a person did this very up-to-date with literature. You are curious. You won't be able to survive our commitment to respect as a data science leader. You cannot be just been executed sitting in their room having eight hours a day of meetings and then go home and start the meetings all over that that's not going to work in either setting but especially the startup sitting I like to have basically coding sessions with individual members one-on-one and go through their code. AIDS line by line and you know, I write my own code obviously but in a larger company setting that might not be always possible practically. It's true really really interesting really interesting another question about the field of sort of in general. What is it about our field that excites you? Why do you love it? There's so much impact that data science can do and is doing it takes just a little bit of careful looking into what can be done and just looking at the influx of Data as we move forward the accumulation of data and when I see companies that have access to that sort of data. I feel like a kid in a candy store just you require the right kid to go into that tennis court pick up the right candy. So we're moving in a direction we're going to everything is being recorded. If enormous amount of data is being generated and companies require leaders to tap into the right talent pool and prioritize accordingly to make that short term impact as well as building a rata g for the mid term and the long term so making that impact across the board obviously being in astronomy for very long time making a discovery looking for interesting signatures from Star clusters. Hoping to find a new type of black hole is very very exciting IT addresses some of the most fundamental questions about how the universe and two stars that are forming the building blocks of galaxies, and the universe is very exciting to me but looking at real world, Problems such as how can we improve Healthcare which is a disaster in the u.s. How can we improve the health of individuals? How can we prevent certain diseases or slow them down? How can we discover new types of drugs with machine learning and deep learning? How can we increase the wealth of individuals? How can we manage wealth? How can we make regulations? How can we prevent cyber attacks and contribute to world peace? I mean, those are exciting things. Well, I Many things that is great and as Donna sighs trust your help on those challenges and obviously with the impact that the field is going to have into the future. What do you see as a current set of challenges and the future some challenges that we have in front of us in our space. Again. This is kind of circling back to the cultural transformation aspect of this digital transformation era is I think successfully making that cultural transformation happen is critically important and it will be I think a very challenging especially for larger companies unless they change from top to bottom again. It's never going to happen from bottom to top. It's going to happen from top to bottom unless they change the culture at the highest level maybe hire a new CEO with that vision and implements that culture. I think it's not going to happen and with smaller companies. I think the biggest challenge is to get their hands on the data. I mean if You're a startup. You don't typically have access to the data that larger companies have but they have one big Advantage. There was a very recent nature paper that also elaborates on this with Based on data. Not only anecdotes is if you want to innovate and disrupt your work with smaller agile teams, whereas, you know larger teams are more appropriate for development and deployment at the larger scale smaller companies are like Surfers that have a smaller boards. They have a high. Risk of falling down but they have higher agility and they require less of an energy to write that a I wave whereas with larger companies. I compared them to surface with larger boards add are more stable, but there's more inertia that they have to overcome. But once they overcome that first inertia that they can safely surf today. I wave whereas I think most of the companies right now, there are just behind the eye wave and try to catch up and as many swimmers. I would know if you are behind the wave you have to spend maybe 10 times the effort to catch that wave and once you catch that wave, it's good. But you have to spend a lot of resources in order to catch it. If you're way behind very very very true. This has been absolutely great. But I only do one last question for you. That is a take away. But something that you would like to leave the listeners with to inspiring data scientist. I will say keep your curious a curiosity is your biggest asset and be an avid learner and the second one is also valid for the leadership. I mean unless you're open to learning if you're an avid learner, you won't be able to survive against the incoming Talent that's kind of coming in into the data science world. So so true. Hold on. Thank you so much for sharing your insights your perspectives your wisdom your thoughts and unfilled. I think that you're definitely captured. So much of what people need to be conscious of what they need to be thinking about and establishing teams in being a leader in our space and all smoke when we're gonna zation seem to be considering when looking at hiring and developing this much needed skill that will transform their organizations to the Future. Your perspectives have been absolutely fantastic. Thank you so much for your time. That was wonderful. Thank you for inviting me Philip and just one caveat in what I've just said the field. Out of data science AI machine learning is changing and transforming itself on a day-to-day. Basis what I say today might become irrelevant tomorrow. So this is why I'm saying everybody has to be a bit Learners and adapt and be very Dynamic. This is where we are right now and I think you know the approaches that I just shared with you may be valid for the next year or two. I think it's always good to get multiple very diverse perspectives from people that are both on the business side and Academic side to both sides of the aisle. I would suggest to really pay the respect to the other side that they deserve. Yes. That is very necessary the moment you pay them respect to the other side from both ends. Oh, absolutely. That is a fantastic note to end on thank you so much for your time. Thank you for sharing all your interesting perspectives the great talking to you Philip. Thank you. I wanted to tell you about the rmit online. Masters of data science strategy and Leadership. I was one of the industry advisors for this program. It's an online Master's program and it covers both a design strategy and Leadership and it has also a technical component highly highly recommend it for people wanting to get ahead with the program. You can gain this Advanced strategic leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions for more information visit on line dot R mit.edu dirt a you 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 utility. Also go to data futurology.com forward size 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.
Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA, and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years, he has led data-driven efforts in R&D and built multifaceted strategies for the industry. He has been a data science leader at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists. Enjoy the show! We speak about:   [02:00] Bülent’s background   [05:50] The transition from astrophysics to business   [08:45] Data leaders need technical experience   [12:45] Academics still need soft skills   [19:20] What data science can offer organizations   [23:50] Addressing causal inferences    [25:30] Recommendations for implementing culture in the workplace    [30:00] How a leader should balance priorities    [36:10] Challenges Bülent currently faces in the industry    [38:15] Hierarchy in the startup space    [40:45] What Bülent loves about data science    [42:45] Future data challenges    Resources: Bülent’s Website: http://www.kiziltan.org/ Bülent’s LinkedIn: https://www.linkedin.com/in/bulentkiziltan/  Quotes:    “Culturally, I was surprised by the mindset of business leaders.”    “We asked individual members of the data science group to come up with their own ideas that can be implemented in the day-to-day business operations.”    “A diverse team is critically important for the business.”    “All companies will become AI companies in one way or another.”    Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information  And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Enjoy the show! 