greg-coquillo.mp3

[00:00:00] Empathy, right, so a Data scientist will have is somebody who solves the problem or comes up with a solution for a customer. Right. And being able to empathize is a clear sign of emotional intelligence and being able to, you know, put yourself at the customer's shoes to fully understand what they're going through. This is how you can relate. So once you're able to walk in their shoes, you can express a clear message that elaborate how you will solve the problem. So long story short, empathy start with empathy.

[00:01:00] What's up, everybody? Welcome to the artists Data Science podcast, the only self development podcast for Data scientists. You're going to learn from and be inspired by the people, ideas and conversations that'll encourage creativity and innovation in yourself so that you can do the same for others. I also host open office hours. You can register to attend by going to bitterly dot com forward, slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show for Five Star Review. Our guest today is an Amazon private brands program manager and content creator.

[00:02:04] He's earned a bachelors in industrial engineering and a master's in engineering management, both from the University of Florida as the head of private brands global expansion at Amazon. He's a tech and Data science to scale new product development globally. So please help me in welcoming our guest today, a man who, if given a chance to do it all over, would have become a sci fi scriptwriter. Greg Coquille. Greg, thank you so much for taking time out, is scheduled to be on the show today and appreciate having you here.

[00:02:39] I could not be more honored by your invite and happy to to talk to you and anybody else listening to your Green Broadcast. So thank you for having me.

[00:02:50] The pleasure is all mine, my friend. I'm super excited to chat with you. So let's learn a little bit about Greg before we get into all of your really interesting and unique experience that that you've acquired over the years. So talk to us about an experience that helped to contribute in shaping you into who you are today.

[00:03:12] So I could go to my my, my, my life story. Right. I grew up in a family of entrepreneurs, but they're also highly educated. My mom has a double majored in engineering. She's a she's a chemical and civil engineer. My father is an economist. But my they're both entrepreneurs. And I grew up looking at or they help me and my two brothers looking at the world as is what what if we could.

[00:03:43] Right.

[00:03:44] So what if we could meaning take a look at a problem and think about what could be done to address those problems. And I've seen my father, my mom put businesses together and those businesses that helped us get an education. So we we all three of us, we have a master's degree.

[00:04:02] And this is because of the sweat of of our parents. Of course, we did the work.

[00:04:09] But if I can take a look at the world today is because of the way they educated us to always look at the world as if you are able to contribute and solve a problem to help you manage to live a better life.

[00:04:25] That's beautiful. Man is such an empowering mindset to have growing up. So when you were in high school, what did you imagine the future would look like for you?

[00:04:35] So, you know, coming from Haiti, it's it's a very hard terrain to navigate for a parent to be able to provide the best education for their kids. So I thought I was going to stay in Haiti for an education, but I was blessed to have, you know, parents who are able to afford the universities overseas for us.

[00:05:00] So they we all went to the United States. We paid international fees. We have no student loans. So I actually thought that my life would be, oh, I'm going to get a degree in Haiti and then to work for Haiti. But having parents who are able to provide us with a better education by sending us to a country with a better education system. Now I am where I am now and I'm very grateful for that.

[00:05:26] Is that completely like you mentioned, it sounds really different than what you imagined life would be. So something like if you think about the world, how it is now, right. Like when we in high school, we're probably the same age, like Amazon, like, you know, it didn't exist back then. Right.

[00:05:43] And yeah, exactly. Exactly.

[00:05:45] So, yeah, like the global economy, this type of online economy, like, what did you think you would be doing in terms of work?

[00:05:53] So for me in high school, I really thought that it was more of a I knew I wanted to be an industry engineer. So when it comes to factory, for example, I was fascinated by the fact that you could see a tennis shoe, a pair of tennis shoes replicated ten times, 100 times. Ten thousand times. Right. And with very little variation inside of that and that tennis shoes. So I really thought, OK, I'm going to be an engineer like my mom. I'm just going to focus on that. But the outside world, yes, I knew about it. There's a huge influence of United States over Haiti culture. But you know, the terms of me seeing me working for a company like Amazon out of the Blue Man, it was more of a, you know, I'm going to be part of a factory in helping, you know, with the factory metrics, productivity and things like that.

[00:06:46] But never have I seen myself in a tech company.

[00:06:51] So what was the journey like after you had graduated? I've gone through school. Kind of walk us through the path I can lead you to where you currently are.

[00:07:01] Yeah, and Data nothing planned, right. So I wasn't a, you know, looking at it today, I can see two things. Yeah. There are things that I could have done better to accelerate my career, but also the route that I took was what I was supposed to be that got me here today. So I was blessed to graduate in as an international student, as you know today, the things that international students are going through. You know, there was stuff in I as an international student, I graduated in 2008, 2009, actually, for my bachelors right. In that direction. And, you know, continue with my masters to graduate MOOCs in 2010.

[00:07:48] So I was blessed enough to have a company, you know, take a chance with me and hire me because I had a work permit. So I was blessed there and started an industry engineering as a process engineer in factories and then with Publix manufacturing. And then I went to a company called Everydayness. And that sale labels in on different substrates you can think about like a bottle of beer, pharmaceutical bottles, you name it, or clothing industry. And there I had the opportunity to grow and this is where I was more grateful. So I had rotational leadership development program where I had I was part of different positions. So I had a chance to explore Supply-Chain, explore quality, engineering, safety, engineering. I explored operations, management and things like that. So those are the things that helped me grow and really cement my, my, my technical and managerial skills and business skills. So and then after that, I moved to another company, microbial control company, where I was a product manager.

[00:09:00] In there, I was really cementing my business skills where I was focusing on pricing, management, pricing analytics, margin management in really the full global product management gig. And this is how I was able to sell my story to Amazon, because while I was there as a product manager, I had an opportunity to build a service that really helped people around me making their lives better. So in other words, where I was, I was supporting commercial teams. So sales teams, it took them forever to quit quote. It took them about two weeks. So me looking at Data, I said, OK, why can't you access Data a little bit faster celebrating the accreting Data products that made their lives easier?

[00:09:49] So now I increased the company's productivity and I was able to work some orders there. And Amazon was fascinated by that. And here I am.

[00:09:59] That's awesome. And was a lot of this was obviously like self-taught. You kind of had to teach yourself the tools, I'm assuming. What was that like?

[00:10:06] Absolutely. And this is where for me, I always say that you will always hear me say that is to let your curiosity be your driver. Right. So for me, the world of Tablo data analytics, you know, it was kind of exploding. And I saw that this company had a contract with Microsoft Stool's including Partovi. So I'm like, OK, I want this. I want that. I started taking classes and paying for them myself and started to learn it and build tools for myself. And, you know, looking at a commercial person, somebody in sales, looking at their process and doing it myself and then creating a better process for myself using these analytical tools like Barbot. And then he started to click. And then I got to be stakeholders. I say, this is what I built. Can you try it in asking questions? Because they are the subject matter experts and they are able to adopt my tools. And it became a tool that was now used for bigger things where now they are sort of using it for sales and operational plans for next year. They're using it to explore price variance when it comes to different currency exchange rates. Because my two had more than forty nine currencies across the globe. We spoke to the different marketplaces where we did business, and so it became a tool that had access to all sorts of tables and at the back end, in creating that quick analysis for insights and so they can take quick actions. So all of this is just curiosity. That's it.

[00:11:57] What's up, artists? I would love to hear from you. Feel free to send me an email to the artists of Data Science at Gmail dot com. Let me know what you love about the show. Let me know what you don't love about the show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register like going to Bitly dot com forward, slash a d. S o h. I look forward to hearing from you all and I look forward to seeing you in the office hours. Let's get back to the episode. That's awesome, man.

[00:12:43] You've got a really interesting and unique set of experiences, insight and understanding that you've been able to accumulate throughout your career. And as someone who is walking in both the worlds of Data science and product management. I wanted to pick your brain on the relationship between Data scientists and product managers. This is something that a lot of my mentees as well as myself are really interested in and would be awesome to hear the perspective of somebody who's kind of been there before. So what role does the product manager play on a Data science team?

[00:13:23] Yeah, so, so long story short, and I can say that the product manager is the team member inside of a Data science team has to start, you know, to work together. They each feel their own functions. But I think a product manager is a member of the science team.

[00:13:48] However, you know, it depends on how you and the company culture see a Data science team. Does that company see the Data science team as a team who tackles projects or produced products? Right. So if that Data science team produces a platform that puts out products, therefore that product manager is there to take a look at the product vision, take a look at the vision, take a look at what the customers need it what to translate that need into a business need. And the data scientists think that business need transform it into a technical requirement. So really, it's about building that pipeline of customer needs as a product manager, taking a look at what that long term vision is for those Data science products. Right. So and I say products versus project because it's different. Your project is kind of like presenting a business case and say, how do we fix this and how do we sustain it? But a product has a lifecycle, right? You create it, you deploy it and you do business with it. You create value for your business for years. And you think about, you know, the time where you will finally deprecate it. Right. It's a whole ecosystem. So a manager is there to guide through the vision.

[00:15:21] Thank you so much for that. And that's, you know, very, very eloquently put. I don't think I've ever heard it put so clearly the difference between a product and a project. That was really good. Thank you for that. So what part of the Data science lifecycle does the product manager own is as a kind of like they're there throughout the whole process? Or is there a particular piece where they kind of put their stamp?

[00:15:44] It's like throughout the whole process, right. So when you go when you think about a product, you're adding different features that are product. So what's guiding those feature enablement or feature add additions is the product manager being so close to the customer? So in other words, throughout the project or the implementation of that product or development of that product, the product manager is the voice of the customer. That's it. So when it comes to taking the pulse of what's out there, he's the voice of the customer when it comes to measuring whether the customer is happy with a new feature that you released, that product matter is there to tell you fulsome metrics and validate that this feature is, in fact, doing his job or this feature needs some improvement and would go to the Data science team to take a look at explore different ways to increase the customer experience.

[00:16:47] Thank you very much for that. And so there's a question that somebody asked me during one of my officers and I was kind of stumped at at. At the at the questions, I don't think I had a good response for it, so I'm here to ask you. So hopefully it help me in my thinking here. So how is a product manager different from a manager of a Data science team?

[00:17:11] That's that's a good question. Right.

[00:17:14] So if you think about a data science product at conception, you always start with a business problem where both the Data science manager and the product manager is part of.

[00:17:31] But the Data science manager is there to look at the big picture in terms of how do we solve that problem, that problem, that business problem. First of all, translate it from a business problem to a technical problem. But now what kind of team do I need? Right. I mean, is it a new problem?

[00:17:52] Do I need research scientists who are now experts and crunching data to analyze similarly estimate, you know, come up with a new model to explore, you know, that set of data that speaks to that particular problem? Right. So the Data science manager is the one who's bringing all the team together that he or she feel is needed to orchestrate the solution to that particular problem, not the product manager. The product manager is more customer facing remains there and feeds that data science manager with everything so that Data science manager to make the optimal decision for that optimal team forming an amen.

[00:18:44] Thank you. Thank you very much for that. So what can the data scientist learn from the product manager?

[00:18:53] I'm going for a huge kudo's to Data science managers because in in in a very developed team you will see that they are already business savvy people. Right, because they have access to the data. They can kind of self-restrained. Right. A good thing to do is probably take more, you know, become more aware about the financial terms of a business metric. Right. For business performance so they can work with financial teams, just like a plant manager works with the financial team. They can work a little bit more with the legal team because the legal team is another set of of things that you definitely need to to understand, especially for businesses or international.

[00:19:44] So the regulatory landscape, the legal landscape changes and they should be more aware of these in how these legal landscape affect business in the business performance.

[00:19:57] So it's two things. So the manager can teach them kind of like the financial side of it and also kind of like the legal side of business. That could be a very good, good, good thing.

[00:20:08] So for the Data scientists out there who are knee deep in just doing the code and doing the algorithms and stuff like that, what could they do to develop their business acumen and maybe develop their product since I believe in the power of shadowing.

[00:20:26] Right. So you have multiple people in the business on the business side who are willing to help? It depends on the organization to read. So to me is let's say I want to be a data science scientist and I want to pick a project to me. One of the things that I have to practice is communication skills.

[00:20:52] It's huge, right. In that communication skill helps me translate that technical solution into a solution that you can relate, that you can have your stakeholder relate to. So in one of the best ways to learn from stakeholders is to invite them into the technical solution, you know, building, building session. So I think that's how I see it.

[00:21:25] Definitely with the power of sharing, man. Just just I guess I guess what I'm trying to say is like trying to get inside somebody's head by just watching them kind of do their thing or questions.

[00:21:38] Absolutely. Absolutely.

[00:21:40] Yeah. What can the Data scientists do to help make their product manager more effective?

[00:21:48] So one thing definitely a product manager needs to know is to have. How to manipulate is how to manipulate data, right? Have a great understanding of the databases, where does the data come from and things like that.

[00:22:05] So I have a high level of capability to query data and be able to integrate. That makes the life of data scientist very easy because it will reduce the load of questions that they will receive from a product manager. Right. So having the product manager who's independently capable of carrying data in making decisions or making interpretations themselves or pulling insights themselves is a very helpful thing. So if a scientist can help part management with that, it would be a good thing.

[00:22:44] And on the flip side of the coin, how can a data scientist learn product management skills?

[00:22:51] I can take an example on me, for example. Right. I'm on the business side.

[00:22:58] And one thing that I do to help data scientists is life a little bit better is to create Rupertswood repositories of business systems you make use your stories. For me, my repositories is based on regulatory language, safety, language.

[00:23:21] It's a lot of terms. There's a lot of classifications of products. All of these classifications speak to some sort of our business performance is having that Rupertswood repository allows the scientists to learn a little bit better about the business in what business leaders care about, what they need to focus on to either increase the customer experience or increase the profit for the business.

[00:23:53] I like that. Just like a keep, like almost like a dictionary for yourself that you can kind of refer to whenever whenever you need to.

[00:24:00] Oh, it's even even more than a dictionary. It's kind of like a repository you should have for any business use case that you have. So if if you're trying to build a classification tool for our products, for to to to expand your business in different countries, then you need to have some sort of repository in terms of business terms that you can help the data scientists learn to make sure so you can have another business use case to have a different type of kind of dictionary, just like you say. So it's kind of a living changing document that everybody can learn from.

[00:24:40] Thanks very much for that and appreciate that. So I want to get into some of the really cool posted on LinkedIn. I think you have such insightful content out there. I always learn something every time it pops up on my feet. And you posted something really insightful I like recently about the ten dysfunctions of product management. So can you talk to us about a few of those that that really stick out to, I guess, the most dysfunctional pieces of it?

[00:25:11] Yeah, I do remember this this post. I think when I was doing it, I liked it because I saw one that I'm seeing a lot of teams, including mine, dealing with.

[00:25:25] It's the obsession with internal metrics. Right. So we we obsess so much that we forget to keep an eye out on what really matters. Right. So and I can only speak to to to myself. We have we each have a team of scientists or engineers as engineers or able to pull data and put metrics together and things like that. But we become so proud when we finally create that pipeline, create that visualization. We news metrics around this metric that we continuously focus on changing it or figure out how to make it better. When we forget that here we are building one hundred metrics and only five of them really matter. So to me and I'm trying to pull it up here, I think it's called the counting house. This one. Oh my goodness, it doesn't matter.

[00:26:32] I've been to four every single employer. I've seen this obsession where we we put too many metrics up on the board and we forget each year when we prioritize which ones should be discounted, which ones should matter, you end up with a dash where they just have numbers everywhere and all these crazy graphs and stuff.

[00:26:53] So. How can we how do we make that determination then, if if everybody wants to measure everything, how do we actually measure what really matters and how do we determine what matters?

[00:27:08] I think to me it's more of a group effort, but has to be led by scientists who are able to design key research, key testing. Right. You have to test your audience. You have to test your customers, put a product out there and tested and gather the results in and figure out what the customer cares about. Right. So if if you run some tests, you'll save yourself sometimes in terms of what to focus on. Right. So if if customers responds highly to a particular metric or particular feature in your product, then build metrics around that feature so you can make sure that this feature continuously improved through iterations. So I think a very robust testing's can in testing result interpretation can help you get there.

[00:28:10] Yeah, that couple, I think would like you look a little bit of not a little bit, but sharp clarity, just actual clarity on what it is that you really care about. So all of these dysfunctions that that you've posted about which one do you see Data scientists or Data science teams potentially spinning their wheels around the most?

[00:28:35] I think they spin the wheel around. I'm going to say metric in terms of performance of their models. I've seen a lot of things there. Here's why.

[00:28:47] When you don't invite the business stakeholders into your model building sessions, you will miss out on capturing the level of risk that those business stakeholders are willing to take. So you build a model that is, you know, ninety four percent accurate. Is this what the business stakeholders are willing to accept? And then when it comes that time, business stakeholders saying, wait a second, what does 94 mean for us? How does it affect our business? You know what? If, you know, we're in the six six percent in the wrong. And what impact does that have on my business? And when we have Data science teams, PROVENGE sells into, you know, we increase the performance of that model by two percent. We're at ninety two point ninety four now where it's kind of like a battle. Right. And you can avoid that by, again, pulling everybody together from the get go. It all depends on the business case. What are we trying to solve and how much risk you're willing to take is ninety four percent good.

[00:30:00] So for me, I can tell you in my world I can say that I use a computer vision to take a look at a product. Right, like the label of the product or the detail piece of a product. If the computer vision spits out an inference that ninety four percent confidence level.

[00:30:21] Am I comfortable with it? Yes or no? Just a quick example I can give you. Right. How does it impact me for, you know, these multiple instances? Right. You have millions of products. If some of that fall in the six percent, what does that mean for the business? How much do I lose then? I'm willing to take that risk.

[00:30:40] Thank you very much for that. And to my next point, because you have another great post about the difference between A and B, can you talk to us about what that difference is?

[00:30:52] So to me, B.I is measuring check in measuring somebody who is a good bye is able to communicate well. Research will interpret well, in other words, query, understand the background, where does the data come from and things like that AIs more higher level.

[00:31:16] So B.I can used can be used to test the efficiency and effectiveness of implementation. So yeah, it's more of a high level view if you look at an organization as a whole and try gaining, truncating it to different say, business processes and deciding which of these business processes can be automated and what are the tools, what are the systems that you can build to automate and when. I mean, automation is not necessarily replacing the human, it's more of a augmenting the human can. Abilities. So now once you're done building that ecosystem of A.I., which has so many components, you know, a lot of people think about robotics and things are really so much more than that. It's a whole ecosystem when you take a look at the ecosystem. You can truncate it, too, and put some B.I checks inside of it to make sure that it's effective throughout. And when you see the trends go in a direction you don't like, what are the things you can do to improve? So at the end of the day, it's when AIs apply for a business because they want to do it more effectively, more efficiently. They want to save money. They want to optimize user experience. They want to increase profits. That's it. And be AIs.

[00:32:42] Therefore, the pulse and the pulse and what makes for, I guess, what qualities to think make for a good B.I leader.

[00:32:52] I'm going to go with communication. Analytical skill and communication skills are probably the best you could be the best person with who's able to query Data and perform some statistical analysis. But if you can't connect with your audience, you will have a hard time. So I talk to qualities.

[00:33:16] And would it be the same qualities for a leader as well?

[00:33:21] Yes, but also who's able to step outside of their own world and see what's out there? What are the outside trends and how do you stay ahead of that? Because A.I. today is seen as something that is just starting and only accelerating. So a good a person that's good for you is able to see future trends and is able to already think about the different things that he or she can do to implement it inside of the business or at least position the business in a way that will minimize impact and increase adoption. So you have to have that kind of foresight for it to. So I give you a quick example. You implement an AI system. Yes, you will eliminate certain jobs, but how many other jobs you will create with that inside of your organization? You have to have some foresight for that. You have to have some good analysis for that to be able to see it.

[00:34:26] Yeah, it's not like we're splitting up the same number of jobs that have been around since the Stone Age. Right. The world is constantly changing and we constantly need people to do different tasks. And the jobs that my son, my son is six months old and the jobs that he has available to him when he's my age, we're going to look back and I go, that's just that's insane. I mean, exactly what can I and by people do to help make each other more effective?

[00:34:58] I think I think it's more of a great collaboration.

[00:35:03] And I did a post on that to EHI versus by.

[00:35:08] I really do think they so to me, I think I always needs buy to test and measure its performance.

[00:35:19] I think that's the best way to to go get a loan or go along. And, you know, I don't think there can be a I without B.I or vice versa. You can't build a system without being able to test it and perform some controls in order for you to make an adjustment. And that's that's what we AIs for. So I think they go in there and, you know, it's it's it's not a high effort at this point.

[00:35:54] Yeah. Thanks very much for that. I really appreciate that, because I think some people, they know they hype up like being a data scientist. They hype up like, oh, Data science machine learning like this is also great, but like Data science itself, it's huge and it includes like B.I as well. And that's like a very important function, very important role, because that's essentially it's connecting the work you do back to the business so that you can measure and assess the impact that it's having on the business, which ultimately determines whether you're going to have a job or not. Right.

[00:36:27] Yeah, absolutely. And let's not forget, A.I. is used as well. You know, I'm sitting here saying I but I must mention it's it's huge, too. What am I talking about here? Am I talking about, you know, just machine learning? I'm talking about robotics. You know, it depends what what a use case, what industry. Right. There are so many industries that look at A.I. through a different lens. Right. So if I look at where I am now with Amazon, you know, is it in the world of natural language processing with what Alexa is going, you know, to to be able to have a a human conversation with Alexa? Right. That's a different type of world.

[00:37:15] And then you go to the retail space or the fulfillment centers where you have robotics there that are helping humans be more efficient at their job so we can, you know, do business around the world. So that's a different world also. That is the world of A.I. So it's vast. There's a lot to pick from.

[00:37:40] What do you think will be the biggest positive impact that I will have in the next two to five years on society?

[00:37:52] So I think that we we will we will definitely be a little bit more efficient or faster at what we do.

[00:38:05] I am looking forward to the next 10 years where we leverage it to help the environment a little bit more. Right. So when we think about energy consumptions, when we think about water consumptions, you know, you're we there is no shortage of water there.

[00:38:26] If you think about it, no shortage of water is just that. Earth is not transforming it fast enough given the consumption rate that we're using water. So what what can we use? How can we use a AIs to solve a lot of these problems? And I hope that in the next 10 years, we'll be able to make a bigger impact. So because right now the height is that it's just I'm a business. I use the I and I do a good job of using it to make more money. And I seen it. But they're using it because they want to make more money here more because we want to create a good experience for the customer. But at the end of the day, they want to create value for their brand. But I am very excited to see businesses move in. They are. They are right. You'll hear sustainability commitments for from a company like Amazon is taking. You know, there's a lot of AIs behind that to make that such a huge commitment. And that's what I'm excited about.

[00:39:30] About the flip side of the coin, what do you think would be the scariest or most detrimental to society application of A.I. in the near future?

[00:39:40] The scariest for me is fall under ethics, governance, who has access, who has the right to Data? And also, given that businesses are more multinational, international, how do we create a platform to facilitate exchange of data across borders and who's going to overside that?

[00:40:10] Right. And another thing to do is, you know, third world countries, developing countries there are so behind, you know, I think to me is going to only accelerate advanced countries.

[00:40:23] And is that going to widen the gap between these advanced country countries in Third World countries? So that's a huge concern I have. And, you know, the power is in the information. And if you use a tool to augment how you use that information, that can be pretty scary. And that's where ethics is a big thing.

[00:40:46] That's something I've been really, really curious about lately, is is ethics with respect to to EHI, what does that mean? What does that look like? I mean, we think about ethics and we think about it in terms of how we interact with fellow humans. Right. And, you know, traditionally, at least in in the traditional sense of ethics. But now we're entering a world where we're interacting with systems. We might not be able to tell if there's a human on the other end of that chat. But I mean, you know what I mean? Like or it's really, really fascinating to me, really interesting. And something that I hope to learn more about. But I'm curious, have you done any thinking into to what kind of code of ethics we should have as practitioners of machine learning and I have explored anything like that? Or can you share some of your thoughts around that?

[00:41:43] I haven't explored much, but I've thought about it for sure. And to me, it starts with a simple thing, which is transparency. Right? Let's not forget it's. A one way street, if we take a look at a company like Google, Facebook, whatever, users are willingly creating their accounts. So they're even whether we can discuss, we can be another discussion, whether they read the terms and conditions or not, they are willingly creating their accounts, therefore, given companies like Facebook access to their data.

[00:42:18] But the transparency piece is what can take it to the next level. It's kind of like how do we create a platform where we make that user safe by letting him know. Here's what I have in terms of your Data here's what I'm doing with it. Or do you allow me to do X, Y, Z kind of thing? You know, transparency can create trust and intimate transparency to can help the user understand the value of the data they are generating on a daily basis at every second.

[00:42:53] So to me, it starts there. And who pushes that transparency? That's another discussion as well. Yeah, because you're talking about the government has to take you know, you think about it like companies have internal audit, right.

[00:43:11] For their accounting practices or even their I.T. practices. There's I.T. audit, there's audit for finance and accounting. Is their audit now for machine learning systems? And should there be like should the companies hire an external third party auditing company to audit their algorithms, audit their books? What are your thoughts around that?

[00:43:37] Yeah, so I think it's coming up right globally. That governance is is a huge discussion around the world today. And I think we will not be short of organizations or third party organizations, whether they're affiliated with government, local government or not. They'll be able to make that assessment or be companies ready for that today? I don't think so. Again, this is a simple opinion. It's kind of like are they ready to open up the books and show these third parties what they've been doing with Data? I don't know. I don't think so. But we're going there for sure. If you think about something like autonomous vehicle I've been thinking about and I read about it, too, when you have an autonomous vehicle, get involved in an accident and the autonomous vehicle is responsible because the autonomous vehicle took the wrong turn and turned onto foot traffic and killed someone. So who's responsible? Certainly you can't punish the autonomous vehicle. Do you punish the creators? Who knows? And how does that look like in a different culture? Right. A culture that has got that gives value to different things. How does that look like? Look like does government need to step in, et cetera, et cetera? So with that, I don't think we'll be short of third party organizations or government organizations will be there to kind of lead the way there. At least that's what I think.

[00:45:19] Is this kind of related to this concept of compliance as a service? I know that that's something that you've got a little bit of or quite a lot bit of expertize on. Can you talk to us about that?

[00:45:29] Yeah. So compliance as a service, it's creating simply creating the ecosystem for being, you know, having a green card to do business.

[00:45:40] Right. And I'm trying my best to not use we in terms building that ecosystem means you have a set of of technology softwares where they softwares are processes, workflows, the right teams for to allow any products that you create to get to allow that product to obtain a green card to do business in any country in that green card, you have to have it. You have to meet a lot of requirements. Right. Whether it's a safety requirement, if it's a product you're selling for kids on the products tag, you have to put a claim right on how to use that product, something like that. That's a safety claim. If it's an important requirement, you have to have certain paperwork. You have to show that you've done your testings, you stored your results. You are you know how to trace it back to the last production lot. All of these things is that whole ecosystem that gives that product the green card to do business in the territory. Concern is what I call compliance out of service. So anything that enables that product to go in the market as fast as possible to do you think Data scientists need to know anything about compliance?

[00:47:06] And if so, how do we how to go about upping our knowledge in that respect?

[00:47:12] Absolutely. I think the they are aware of these things already in you know, we're not shy of problems that requires them to help us. And I mentioned one earlier, classification is one of them. How do you classify your products as fast as possible? You know, companies as use as Amazon, you can you can imagine that the the labels are used. A number of labels are huge. And how do you come up with the best classification now on the legal side? There's there's a lot of legal language to when it comes to compliance, what is the best tool to accommodate that?

[00:47:54] So if you think about it, we're very lucky to be part of Amazon because, you know, you have eight of us creating these great tools that allow us to leverage for our business use cases. So if you think about text extract for text extraction in Amazon, Cumbrian for to add context to to the text so we can perform some business decisions and we already have tools there that that can help us.

[00:48:29] Right. Man, thank you very much. I really enjoyed that that bit there. Give me a lot to think about now. Got the wheels turning all about ethics and compliance and definitely to dig a little bit more into that for for my next bit of research. So I'm kind of changing gears a little bit here. So there's always that saying ship before it's ready. Right. Ship it before you ready and thing that totally makes sense. And like the software development space. But I feel like people need to do that with their careers. They need to just ship it. And I think people get scared to do that because of the crazy job descriptions for Data science rules. So what advice or insight you have or that you can share with people who are breaking into the field? They look at these job postings, some of them, they want the entire, you know, abilities of an entire team wrapped up into one person. They feel like, you know, just dejected, discouraged.

[00:49:25] And, you know, that their man is that the term ship before ready definitely should be applied to all of us. Whatever you want to be in the world of Data science or not, you have to be able to take a look at a job description and see if you can fit it as 50 to 70 percent. And and you can increase that percentage by working on a project. A good a person who's good at applying is able to anticipate the problems of the horror and showcase their projects to, you know, to show alignment with that anticipated problem that the IRA is having. Right. You take a look at a job description. Typically, they tell you why they hired they want to hire you. So you have to do the job in terms of picturing or researching what kind of problems that they must be going through right now and what kind of projects that I worked on that is similar to the potential problems they might be having right now and how to our position myself to ship it before it's ready.

[00:50:31] I love that man because it's just you have to think from the perspective of the company, the hiring manager, what do you think they actually are looking to get accomplished in this role, like they might have a list of whatever qualifications are looking for. But you don't look at that list of qualifications. You look at the job description and you look at how potentially your skills can help solve the problems that they're describing. That job description slowly.

[00:50:57] That's it. That's it. You you lose everything if you don't try.

[00:51:01] Exactly right. What's the worst that can happen? OK, you cannot apply for the job and not get the job and not hear back, which is literally the exact same result that would happen if you were to apply for the job and they didn't reach out. I guess I said that backwards. So let me get a fixed and we'll fix this on the editing. But but you can apply for the job and not hear back, right. Or you could not apply for the job and not hear back anyway. The result is the same. So why not just go and go for it and apply for it.

[00:51:35] Exactly. And let's not forget, you know, the hire or if he's a scientist or she's a scientist, he's able to take a look at the technical terms that you put in your, you know, your resume, but be also open to writing. It in at the most layman's terms possible to increase the audience, who's going to read your your resume? Nowadays, companies get more than just the hiring manager to interview you. So if somebody on the business side is reading your resume, is that person able to understand that the projects you worked on correlate to or is similar or similar to the issues that he or she is having?

[00:52:22] Thank you very much. That man I mean, I hope people at home are listening and just really just take the step. And I know so many people message me as part of my mentee community, like so scared of everybody else's skills. I'm so scared of all these job postings. And you don't have to be just just go for it. And then and what's the worst that could happen? You don't hear back. OK, well, worse things have happened to people. So there's an interesting post that actually I saw you post right before we had jumped onto this call. I wanted to to have you kind of unpack that for us, this concept of first order and second order thinking that was really fascinating. And you mind talking to us about that?

[00:53:07] Absolutely. First of all, I need to give full, full, full credit to Danny Sheridan, who created Facts of the day one, I believe, a couple of months or under a year into joining Amazon. He created this where a lot of people are now tuning or tuning into it.

[00:53:25] And it even has a LinkedIn page. And I get daily emails from Facts of the day one in.

[00:53:34] I learned tons. And, you know, I love sharing with my audience what I've learned. So this is where I've pulled it. But first principle thinking the first time I'd be I'll be frank, I've learned from it is hearing Elon Musk talk about it is being able to bring down an issue to down to the fundamental truth that no one in a room of experts can change. Right. But if we think about it in terms of I want to become a data scientist, well, what does that mean? You have to take a look at that goal as a tree with branches big and small in leaves. Those the truck is the base that you need to know to become a data scientist. Then you have the big branches and then the leaves is kind of like, where do you want to be in the data scientists data science ecosystem? You want to be a data engineer where deep dove into that, go into the leaves, study it, learn from it.

[00:54:42] Then when you're done with that, you practice it because we tend to forget retained, but we forget over time. So practicing what you learn will cement, you know, your expertize. That's first principles. Thinking in second order thinking is a different thing. So I'm going to use a different use case. Then I want to become a data scientist. It's kind of like just like the Post. It's such a great example. I will create autonomous vehicles that run on batteries. So second order thinking is simply asking yourself in your group, asking themselves what is the impact of that? Right. What does that mean for gas companies? What does that mean for energy consumptions? What does that mean for convenience stores at the gas station, all of these things, and start taking down these levels, you know, peeling the onion to figure out whether the new solution you're implemented doesn't have very impactful effects downstream that could be even hurtful to the economy or overall balance it out or even make it better. So you have to be able to get that second order thinking going once you create a solution.

[00:56:09] Thanks for that momentum. Definitely to follow them and get some of those emails as well. That's really nice, actually, seeing you post a few shares from that fact of the day one. And it's always amazing stuff, man. So definitely, definitely. I'll post that into the show notes as well. So we're talking a bit about, I guess, non technical skills that a Data scientist needs. So you mentioned communication being one of them. What I guess, what is the importance of communication and how can a data scientist build a strong set of communication skills?

[00:56:49] I think to me the first thing is to be able to practice. Empathy, right? So a data scientist will have is somebody who solves the problem or comes up with a solution for the customer. Right. And being able to empathize is a clear sign of emotional intelligence and being able to put yourself at the customer's shoes to fully understand what they're going through. And this is how you can relate. So once you're able to put yourself in their shoes, on their shoes or in their shoes, you can express a clear message that elaborate how you will solve the problem. So long story short, empathy start with empathy.

[00:57:48] Absolutely. And that's like the most important skill I think I was. I'm actually writing a piece. It's on persuasion and it's a epik framework for persuasion. I see. That's an actually perspective taking influence and concurrence. Right. And it all starts with empathy. If you want to come, you think about a you're data scientist, you might think you're a scientist, but you are in the business of sales. You're moving people. You are moving people to part with resources, part with time apart, with their old ways so that they can adopt your new methodology. You are in sales. You're not selling individual products. You're not cold calling people, but you are selling your ideas and you're selling emotions. That's right. Yeah. And it all starts with that that first part of the framework, the implicit understanding what somebody is thinking. Once he can understand what they're thinking, then I mean, sorry, understand what they're feeling. Once they understand what they're feeling, you can understand what they're thinking. I think we're emotional creatures that human beings are driven by emotions as much as we try AIs Data scientists to train the emotion at first, with all this rational, logical thinking that we do, we cannot escape this basic fact of human nature. Exactly. Do you have tips for data scientists when they're in a room full of executives and they're trying to communicate and sell their ideas to a non-technical audience? What can we do to make sure that we're selling our idea in such a way that it has the best chance of being bought?

[00:59:29] I think the best thing is to always leave the door opened for the business stakeholder who's often the decision the decision maker leave the door open to to interpret it different, different ways. So provide kind of like in a room full of business, stakeholders always provide a list of prescriptions and those prescriptions needs to clearly elaborate the business scenarios or business outcomes.

[01:00:00] Always do that. If you do X, X, Y, Z will happen. If you do Y, A, B, C will happen with a chance of Z, something like that. Always leave that list of prescriptions in that list of prescriptions needs to be clearly communicated, time bound Data sound and speaks to the business processes that those stakeholders are fully aware of.

[01:00:30] So Greg, last formal question here. Before I jump into random round, it is one hundred years in the future. Is the year two thousand one hundred and twenty? What do you want to be remembered for?

[01:00:48] Freakley, I want to be remembered as one who helped with the next frontier of Data management outerspace.

[01:00:58] I like that. Elaborate on that a little bit more.

[01:01:00] That that is kind of like, OK, we have a lot of the data centers here on Earth, whether we shoot probes in the universe and we wait years for that probe to send back messages or Data we start at Data on Earth. Why can't we store data centers on other planets? I think it's already happening, too. I want to be one to help with that. It's crazy. But can you imagine Data sentence?

[01:01:35] Not until this very moment had I imagine that putting we can put data centers on the moon. That is that's that's a bit of your your your sci fi writing coming out, huh.

[01:01:46] That's pretty cool. And that's it. That's it.

[01:01:50] That's really cool. And that is that's what that that's. I like that.

[01:01:54] And now with all of these billions of planets, think about how much Data we can harvest and store and learn from. So for me, the next hundred years is really looking at how is the Internet going to help us there? What what's next after the Internet? Right. How do we how do we transfer a data faster and more secure? I've read a couple of things about quantum computing and how it would create a safer way or a faster way to transmit billions of data in a split second. You know, I'm I'm really curious about that. The world of quantum computing. And definitely I want to be remembered for the work being done as a contributor for the next level beyond Earth and whatever we do to protect Earth.

[01:03:01] That's how smart. Absolutely love that. So let's jump into random round here so you get a chance to start all over. You can pursue your dream of being a sci fi writer. What would your script be about?

[01:03:16] It's probably going to be about. Of course, I'm going to use a corny word. It's us discovering that the aliens of the future trying to teach us something is actually us in the future kind of thing.

[01:03:31] Like in a movie called I'm a Guy with Matthew McConaughey or an Interstellar Interstellar. Yeah, that kind of thing.

[01:03:40] That kind of kind of thing. That kind of thing. I like those types of of stories. And now what that lesson would be, I don't know. I would have to think about it and come up with an original story. But that's what the premise would be.

[01:03:56] And I like them and I think I might watch Interstellar tonight. What do you currently most excited about or currently exploring?

[01:04:05] A couple of things. Right. So quantum computing, definitely. I want to start learning or reading and reading more about that. I'm excited about that. From the employment perspective, I want to become a product manager and I want Amazon, of course, and I want to manage an ecosystem of artificial intelligence products. I have spoken about that with my peers and I'm positioning myself for one day I become a product manager in that sense. And another thing I'm excited about is I'm also working on entrepreneurship. So I have other partners. I do have a an integrated marketing company. You know, I've been interested in entrepreneurship since I was in college. I stopped and started again. And hopefully one day I'll be able to not only be brave enough, but have enough resources and enough knowledge, but mostly courage to to start my own company. And I'm really excited about that.

[01:05:14] I look forward to seeing that happen for you, man. That's awesome. Thank you. If you could have a billboard placed anywhere in the world, what would you put on it? Where would you put it?

[01:05:26] So the message is one of my favorite little curiosity, be a driver for growth. And I would probably put it is going to be huge. I would you say it does have to be on Earth anywhere you want it to be.

[01:05:45] Anywhere. I would put it on the moon, put it on the moon and the full moon.

[01:05:51] You see it against the full moon, the colors against a full moon. So you can see it clearly right there.

[01:05:56] What are you currently reading?

[01:05:59] So I read a lot of articles that I found everywhere. And believe me, not LinkedIn is a huge source for that. There are so many content creators that I have a solid pipeline of articles that keep coming. Right. So I have a lot of articles that I read just about every day and I am ashamed. But I just started with Audible's and my first book is Zero to One. And after that I'm going I read the start up and then a couple of books about I am not a reader, so more comfortable with Audible's, but if it's an article, I'll read it and read it again. But I am not a book reader at all.

[01:06:44] Audible's Awesome. I've lived by go through books a week, like multiple a week. So since you just started. You're the one. Let me ask you this question, what do you believe that other people think is crazy?

[01:07:00] I think I believe that we will be a multi planetary species.

[01:07:08] I think a lot of people think he's crazy or are not necessarily just crazy, but they think they just don't think about it. I believe that we will continue to be efficient at transportation because in order to be a multi planetary species, you have to be able to transport certain things that we built here, over there so we can start harvesting the local resources and continue building.

[01:07:38] I love that. That's I hope we get to see something interesting like that happen in our lifetime. At the very least, I hope we find life on another planet in our lifetime. I don't even care if it's just like an insect.

[01:07:52] I mean, yeah, it if you think about it, it's it's coming down to the same thing, man. It's energy consumption. How do we minimize energy consumption to get us from point A to point B, right. When you look at the the race going on right now between the space companies, it's all about recycling the tools and equipments to make it more affordable, to make transportation more affordable.

[01:08:22] So once that's more affordable, just like, you know, I've heard Jeff Bezos say, you know, what else what other technology can we build on top of that once you make energy consumption more affordable and less costly in terms of long travel, long distance traveling? Who's the next level of scientist who will start building on that?

[01:08:48] That's cool, man. This awesome thing to think about. So when do you think the first video on YouTube to hit one million views will happen? And what do you think that video will be about?

[01:09:05] Interesting question. So one tree you I just want to clarify that a little bit. So when you say one trillion, you obviously you want me to watch it 10 times. And that counts as views, right?

[01:09:19] No, no. Just like four, for example. Right. Right, right. I think the most highest view, like the video with the most views on YouTube is like something like seven or eight billion views. And it's just twenty, twenty right now. So when when will that video that that gets one trillion views, like when will that happen?

[01:09:42] That's that's a hefty number.

[01:09:44] And we definitely need more population for that. We have seven billion views now with a YouTube video and then I'm just estimating about seven billion.

[01:09:58] And it is Dustbuster.

[01:10:00] This mosquito got seven, eight billion. Right. And I know for a fact that we're only about fifty four percent would people with Internet access. So I don't know the fifty four percent to that. That only puts us. Oh man we're OK. Just shooting a number here. We're so far from this. I'm going to say we're looking at probably 20 30 for this is going to take time. You need, you need more population and you need more access to the Internet, faster broadcasting and things like that. So that's that's my. And what it will be about. It will be I don't want to go about it on something. We are emotional people.

[01:10:49] When it's got too hard, it's probably going to be about some sort of kid prodigy that is explaining a scientific demonstration, giving a scientific demonstration, probably a kid under under 18. So I would say between ten and 18.

[01:11:13] Explaining something, baby son, if you're listening to this in the future, that better be you. So what would you do if you're the last person on Earth?

[01:11:25] I definitely go straight to the books to understand what can I do about agriculture, because I think agriculture can not only help me survive, but also help Earth as well. Right. So Earth can have pity on me.

[01:11:42] All right. We're going to take this one to the random question generator here with the couple out of this one. What do you do on a free afternoon in the middle of. Week or what would you do?

[01:11:55] I have a lot of classes that I follow, whether it's on LinkedIn or Udemy, I typically allocate 30 minutes per classes and I dwell on it. Amazon has a huge repository of resources, too, so I'm taking like classes on product management as well. So any afternoon that I'm free during the week, I'm there making myself better.

[01:12:29] And that I mean, it's it's so many, so many interesting things to learn. And it's a shame that we only have one lifetime and and only twenty four hours in the day. And we have to sleep like life like there's so much interesting things to learn man.

[01:12:48] So and there are so many people are doing so much that I really wonder, like how do you do it. Are you sure you have twenty four hours per day because he feels like what you're doing is worth 30 per Data.

[01:13:00] Yeah, yeah. I definitely definitely share that sentiment. What is one of your favorite smells.

[01:13:07] Hmm. One of my favorite smells. Fried eggs in the morning. Oh my goodness.

[01:13:13] Yeah, yeah. I'm a breakfast guy too.

[01:13:16] Oh yeah. I am tumin and I am a huge fan of eggs, eggs and pancakes. My goodness. Yes, miss.

[01:13:26] My favorite smell is actually pancakes. I get.

[01:13:29] Really. Yeah. Those two men. It's yes please.

[01:13:34] What story does your family always tell about you.

[01:13:40] Kind of like two things. I had a little bit of sneakiness to me when I was little and also clumsiness too. I'm typically the guy who instead of running when disaster is about to happen, I stay and kind of observe, look around and hurt myself at some point.

[01:14:01] So they will not forget those stories and always haunt me.

[01:14:06] With the last one out of the question generated here, who are some of your heroes?

[01:14:11] Some of my heroes. I'm going to start with definitely my my parents for sure, my brothers, because they did achieve a lot.

[01:14:26] And I would say the people who inspire me on LinkedIn, I have a handful that I will consume their materials on a daily basis.

[01:14:39] And I just have complete admiration for how strong they are, how persistent they are in terms of moving the needle and producing and helping society. And I really like that.

[01:14:56] How can people connect with you? What can they find you online?

[01:15:01] Definitely on LinkedIn.

[01:15:03] So you do what you do to slash Grego Kiyo on LinkedIn you will find me in the Cuchillo is g c o u u i l l o. I reach out to me only then I'm pretty open. I do have a backlog of messages, but I make an effort at the end of the day or during the weekend to take a look at it and respond to messages.

[01:15:32] I'll continue to produce, share, learn with you all and I'm very open there. So hopefully in the future I'll also start being more active in other channels like Twitter or Instagram. But for now, feel free to reach out to me on LinkedIn.

[01:15:49] I'll definitely put a link to the show notes so that people can can follow you. Greg, thank you so much for taking time out of your schedule to be on the show today. I really, really appreciate having you here.

[01:16:01] Harp. It is was it was my pleasure and honor. I thank you again. And I hope that it was fruitful to you as it was fruitful, fruitful to me in looking forward to doing it again. And I hope you have a great rest of your weekend. And I say hi also to the audience, and I hope you guys have a blessed day.