KEESA: Today joining us for our podcast is Hayley Caddes. Hayley has a background in chemical engineering and served as lead data scientist at Decode M, a market research firm where she founded and built their data science practice and developed analytical tools with NLP, for companies like Lyft, Airbnb, WeWork and many more. She is now NYC Director of The Knowledge Society. Hayley, thank you so much for joining us.
HAYLEY: Hi, thanks for having me. I am excited!
KEESA: So, Hayley, when you say that The Knowledge Society solves some of the most important problems of society today – what are we talking about? What are those issues and solutions that you’re coming up with?
HAYLEY: So, The Knowledge Society is actually a STEM Accelerator program for high school students. We have it in a bunch of cities all across America and Canada. The students get to help figure out how to increase women and girls or inclusion in the digital economy, so they're working with the UN on that project right now too. So, it’s kind of a wide range of skills that the students get from the knowledge society but really, it’s training the next generation of leaders.
KEESA: And clearly, they are brilliant at the starting point as young people so great. What an opportunity. What is the role that data science plays?
HAYLEY: Right now, it doesn’t actually play a big role. I've actually kind of moved in terms of managing students and tracking data. I've started using tools, things like Airtable. Just because it’s more user friendly for the team as well. So, what I do is leverage my data science background, more or less to figure out what is working in terms of coaching students on being able to solve the world's biggest problems and what isn’t. So, I used more of my analytical background for that, but don’t use machine learning models for that.
KEESA: So, we talk about finding synergies in strange places, I'm thinking chemistry and then learning and then data science. There are so many different things you focus on in your career. What is underlying all of these different characteristics and disciplines you work in? What skillset has been most important?
HAYLEY: A good question. I think the underlying theme for me personally is really about impact. How can I maximize my positive impact on the world? For instance, with data science, I was working with these companies one on one with the team at Decode M. so I can make impacts that way. Or I can train these young people who are all going to go on in to make an impact so I've maximized my impact on the world ideally. That is why I got into data science, in order to be able to make a bigger impact on the world by leveraging it more than chemical engineering at the time. That's the underlying thing and the most important skill is having a ‘figure it out’ mindset. I pretty much follow what I am curious about and what is important to me and then I have confidence that I can figure pretty much anything out if you throw it my way. So, I think that’s allowed me to switch between such different roles and industries.
KEESA: And for many folks who are listening, people are thinking about contemplating changing careers. Even delving deeper into their existing careers. You professionally train and support education and learning for these young people. Are there huge differences between the learning path of a young adult or teenager? Are adults who have been in the industry for several years – what would your approach difference be in terms of training an adult vs a child in how to think and how to develop themselves?
HAYLEY: full disclosure, I haven’t worked with adults in the same way I have worked with teenagers so take this with a grain of salt. But I actually think you can use a pretty similar approach. So, what I do, if you wanted to train yourself on something, personally I have with data science for example, how do I learn data science? Almost like meta learning. I need to learn how I am going to learn data science for example. So, I make a map for that, and then I figure out resources I have at my disposal whether it’s courses, like cohort-based courses, I like those usually working on projects with other people, then I think about why am I learning this? At the time, I didn’t end up going into it, data science, I wanted to work in biotech and pharmaceutical industry of drug discovery. So, at the time, how I learned is I just did a bunch of projects revolving around data from pharmaceutical industry or drug delivery data etc., so I think doing projects to practice the new skill that you’re learning but doing projects that you actually care about and you are working towards. It makes it easier to get stuff done and connects on a deeper level. Then if you are discouraged you are still excited by the prospect. Because learning on your own and as an adult is tough, it’s about understanding the process is not going to be perfect. Think about who can you talk to, go to for feedback and help and for guidance too.
KEESA: so, what am I hearing from you Hayley is the hands-on approach as opposed to other types. And then building a community of like-minded folks and learners.
HAYLEY: You can do it easily digitally now. You can go on Reddit or Stack Overflow to learn data Science or Discord to get help online.
KEESA: With all disciplines and theories you’re learning and teaching to others, what excited you the most in terms of the future? What disciplines and ways of approaching things you’re seeing now that might catch us by surprise in coming couple of years?
HAYLEY: There are a few things, specifically, there are many technologies it is hard to choose one. One I'm excited about is Space technology. So, we all know about Elon Musk and SpaceX, but there are so many other companies that are working essentially years ahead in the future. Preparing when humans can travel to and from earth to space. What's it going to look like living in there, commercial space travel. And there are a ton of companies in that space and working with the assumption we will get to that point. I don’t know if it will take people by surprise, but that’s what really is exciting for me. A lot of my students are working on that. Another exciting technology for me is quantum computing as well. A lot of people have heard of quantum computing but don’t really know what it is. It is essentially going to exponentially increase our computing power, so our normal laptop might take tens of thousands of years to do a calculation but a quantum computer can do it in a minute. It's going to speed up our ability to collect and analyze data and really to build other digital tools that we probably don’t even know exist yet because we don’t have commercially available quantum computers and that will accelerate a lot of other technologies and innovations.
KEESA: just to dive deeper – who is building these quantum computers? Your students?
HAYLEY: So, a lot of companies you can essentially tap into their cloud quantum computer server. You can actually do some products using their quantum computer on the cloud so that’s what they’re doing so they’re not actually building it. They are using their software side of it but the really technical challenge right now is the hardware and actually engineering something that works.
KEESA: It’s only a matter of time before your students start building them out. They sound brilliant. In terms of learning, what has been the most interesting thing that you’ve learned from your students? Many times, we think the teachers always pass the knowledge, but what have your students taught you?
HAYLEY: Right, that’s a good question. There have been so, so many things. I think one thing that they constantly teach me is the power of following your own curiosity. That can take you really far and makes life more enjoyable. A lot of these students are used to slugging through online high school, college apps and SATs so giving them the opportunity that they can choose any technology they want and explore what they want, seeing them grow so quickly and having someone support them in that and having them do the projects on their own and figuring out coding on their own, that’s one. Secondly, something I am passionate about how young people, like specifically Gen Z, are dealing with their mental health. They are much more open about it than previous generation. Theres a huge lack of resources and understanding with their mental health. It's so foundational, anything with technology or high school or with life, if you don’t have a somewhat solid foundation in terms of what strategies do you use to just manage your emotions? So even if you are not struggling with a mental illness so to say, how do I develop emotional intelligence and coping strategies in general? That's a linchpin for my students and that’s true for many people.
KEESA: From coding to emotional intelligence. It sounds like great work that you’re doing. Hayley, thank you so much for joining us.
HAYLEY: Thank you.
KEESA: Veibha Subramaniam is currently VP of Technology AnalytixInsight. Veibha leads the technology team and spearheads the AI and machine learning initiatives there. She has more than 20 years of the tech industry experience and has been part of technology initiatives at McDonalds, Canada Life and CitiBank just to name a few. Thanks for joining us Veibha! Tell us about the role AI plays in Market Data, specifically Market Data analysis that drives decision-making.
VEIBHA: Well, there is hidden figures and hidden data between the numbers. Numbers give facts, but if you read numbers in association with other numbers, it gives you a flow of information that is a pattern. This pattern is Artificial Intelligence, and that’s what machine learning is all about.
KEESA: So, in terms of who uses this. The end user. Would they be primarily researchers or primarily portfolio managers trying to understand patterns? Who would be the primary consumers of this type of market data infused with AI?
VEIBHA: Well, there are 2 answers to this. The broader answer is that it can be used now that the digital age of something like Robin Hood, when everything is so global and everything is so democratized that anybody can do anything and market the information that you have is instantaneous to anybody without lag in information. This information can be used by almost anybody who wants it in the sense that there are portfolio managers who want to use it to buy a certain data and now you have these mean stocks like GameStop coming in. When it is even given to users of Robin Hood. So, this can be used by anybody and I'm only talking about financial data but can be used by anybody interested in buying and selling stocks. This is in the larger picture. In the smaller picture, what we are doing is trying to cover stocks across all 49 countries in this world. If you see most of these stocks covered it’s usually mostly in US and Canada, but there are also stocks in these countries which are not covered because it’s a small cap or pre-revenue stock. There are also stocks all over the world. If you want to know analysis of a stock in Romania, well they won't have 1500 analysts covering the stocks because there are only 70 stocks. This is where we come in. We do machine learning and AI and give analysis across all stocks all over the world.
KEESA: So, this is great. I'm glad you brought up Robin Hood and GameStop. We've talked a lot about the democratization of data and how it is becoming increasingly accessible to more and more people. Are there some challenges or cautionary tales we need to take into consideration? In terms of ensuring we continue to give data but also in tandem to educate users of data. Please talk to use about that.
VEIBHA: this is very important in democratization of data because the first thing you need to verify is the data source. Now, anybody can come as analyst or as a person to give their opinion on it. What happened a few months back, was that a company declared bankruptcy but their stock kept going up.so when data is so democratized, many people not educated enough in the act of buying and selling a s tock, in these cases, if you are the last person in the line, then you are left holding the bag. What is most important is you need to verify the source of the data, and the validity of the data. That is most important.
KEESA: As we move forward in terms of looking ahead and right now understanding the most important thing is ensuring the source of the data. Are there trends going on that you can speak to where in the next couple of years there are a couple of other pieces, we might need to think about in order to educate ourselves? If there are trends, what are the top 2 trends that you are seeing? Not right now perhaps but will definitely have an impact in the years to come.
VEIBHA: you are talking about the democratization of data in that sense?
KEESA: Yes, in that sense.
VEIBHA: What will happen is that right now, we are in information overload stage. There is so much information coming in and we are not able to make a correct position out of it. So, I think we are in the churning stage right now where it is viewed all over. What will happen is out of this will come out democratic people who are not single-minded and not biased reporters too. So that we won’t have the .com bubble that happened in the early 2000’s. So, there will be certain people who we will start following because they give non-biased information and there will be public disclosure not just the usual ones that come and say that this person is no longer affiliated to blah blah blah. Because of the information overload we are having, it’ll be possible for small start-ups who are not able to get their space under the sun or the information out to the market, might be easily be able to market their idea. Like crowd-funding is something that was never heard of 10 or 15 years back, you’d have to go to someone in the market and raise money for your idea. Now, crowd-funding is something that exists. So, I presume it is a double-edged sword but these are the few trends that I am seeing.
KEESA: And right now, we are talking about your work and experience specifically in the financial space. I know you’ve also worked for other types of industries, McDonalds for example. Is there a difference between data usage and how it’s flowing with financial services versus another type of industry?
VEIBHA: Yes, financial services is blessed with the fact that there is an information overload. There are pieces of data floating and lots and lots of data floating in the data world. The only problem is that the data needs to be clean. The problem with Financial data is that there is a lot of unclean stuff. So, if you want to handle financial data, you have to be a per son who can sit down and clean the data and if you talk to any data mining or data analyst or any type of artificial intelligence expert, they will say that 70% of the time, the time is spent in cleaning data. When you come for information in other sectors for example McDonald's, their data is usually tied to other data points. For instance, they want to know what type of data is moving faster in their restaurants and other stuff so it comes from a certain point of sale. So here the data is relatively clean but there is only one single type of data. With financial data I feel with ESG and Meme trends that’s coming up, the entire democratization of data is affecting the financial world more in comparison to others.
KEESA: And so talking about the financial world, as you mentioned clean data is important. We are really celebrating opportunities for women in technology, particularly in the start up areas, to really look at ideas and act on them. If there are folks who are interested in that type of area within technology, so looking at data, cleansing data, what would you say they need to look at in terms of processes? What things do they need to consider before they go out there and start a business around these?
VEIBHA: well for cleaning data, educational qualifications are that you need to work with math. This isn’t supposed to be daunting. The other thing, I would say as a woman, as a mother and parent, it is patience. Especially when we become a parent, you become impatient but patience is something you need to have when looking at big data and volumes of data. Mainly because to find a pattern and to see how much it repeats and cleaning it and doing this again and again, for you to find one small pattern of data might take you one or two or three months just to find a pattern. As you have to clean, remove the noise, go back again and do it again.
KESSA: For some of us, that’s a different way of thinking. To see patterns as opposed to looking at it from a different perspective, like a linear perspective. What type of approaches do we need to focus on in order to see things in a pattern sort of way?
VEIBHA: The main thing is, data by itself would be a data point by itself. You know, it’s like marrying space to data. As you go in space, the more and deeper we go into space and deeper in we find out that till about a year back, black holes were not supposed to be transporting any information. But now after observing it for so long, we find that black holes do transport information. Similarly, what happens in data is that a data point by itself might be right or wrong but to observe a pattern you have to see it in conjunction with other data points. So, it’s like, there is not much of a difference between observing the world through a telescope and looking at data models and data.
KEESA: Love that analogy, that’s great. Talk to us about your background. You mentioned math and having patience in the industry. Was Math really your path into it or did you take an alternate path?
VEIBHA: Well, my parents are both science graduates so science was part of the family. I was very much interested in space. I'm talking about the early, when the Columbia shuttle launched which was an astounding moment in history. When the first IBM PC was launched, when Microsoft and word were launched all that stuff. So new inventions have really changed with creative paradigm have shifted in the world, that really excited me. Math was something that was I had to work on a lot because it’s not very difficult but not very easy either. That usually got me in the pattern of technology, I was really interested in space so technology was the only thing that could take me towards it. So, I was quite interested in coding and all that stuff. There was a coding class when I was in grade 8 and my mom put me through it in Summer and I took it like a duck to water and that’s where my entire journey started.
KEESA: What was the language?
VEIBHA: You won’t believe it. It is something called Basic, it’s a very old language.
KEESA: That’s great. And from there, what other languages are you looking at now? Python is big in FinTech. What’s big now in different industries from financial services as well as other corporate industry spaces?
VEIBHA: Right now, it is Python and R. those are the 2 main things that you use because it is mainly used because when I started coding 15 or 20 years back. We had to write all pieces of code by ourselves. Now if you take Python, what would take me approximately an hour to code would now take me 10 minutes because now all these libraries are free for use and open source. So I am more interested in finding out the final answer rather than using all the building blocks that we had to build in those days.
KEESA: And that is a great approach to take. Veibha, let me know, you mentioned you started learning coding as a child. If there are adults who are interested in coding, what would you say to them in terms of the best way to start, which language to start, and what kinds of things to be thinking about now?
VEIBHA: I think there is no age too late to learn to code. As human beings, we are programmed to be logical and emotional but we are logical creatures. Even if you are 100 and want to start learning to code, please do! There are so many free resources available that you can find out and so many courses. The first thing you should start off with is Python because it’s really easy and coding right now is very easy. Mainly, given the fact coding is open source and people are ready to share their insights and work as a community.
KEESA: That’s great, so never too old to learn coding, working as a community to learn it is a great way to do it! Veibha, thank you so much for your time.
VEIBHA: Thank you Keesa, thank you so much, it was great talking to you.
