[00:00:08] Lauren Burke: Welcome to Women in Analytics After Hours, the podcast where we hang out and learn with the WIA community. Each episode we sit down with women in the data and analytics space to talk about what they do, how they got there, where they found analytics along the way and more. I'm your host, Lauren Burke, and I'd like to thank you for joining us.
Hello and welcome back to Women and Analytics After Hours. Today we have Supreet Kaur joining us. Supreet is an AVP at Morgan Stanley where she leads the development of cutting edge AI products that will define the next generation of financial tech.
Her experience as a data science consultant covers many industries, and she is a passionate advocate for AI democratization. Supreet also founded the Databuzz community, which is a platform for sharing the latest industry trends and mentoring individuals who are eager to pivot into this exciting field.
I am so excited to have her here with us today. We have an awesome discussion planned around data-driven culture, responsible AI, new trends in the space, and putting yourself out there to discover new opportunities.
So welcome to the podcast, Supreet, and thanks so much for taking the time to join me.
[00:01:28] Supreet Kaur: Of course I'm excited and looking forward to our conversation today.
[00:01:33] Lauren Burke: Absolutely. I am too. And so just to kick things off, could you tell us a little bit more about your background and your career journey to your current role?
[00:01:41] Supreet Kaur: Yeah, sure. In one word, my career journey has been a rollercoaster ride and, uh, full of risks. And that's, uh, I think we can obviously dive deep into what that looks like. But in 2017, I decided to immigrate to United States of America and pursue my masters. I did my bachelor's back in India.
So I left my hometown few 8,000 miles, uh, with two bags, but big dreams and decided to pursue my Masters of Business and Science, uh, which is called MBS in Data Science and then two years it was obviously rough because you're trying to settle in the new country you're trying to adopt to the culture. Uh, but at the same time I was trying to learn data science. I didn't come from a quote unquote engineering background. So that was a learning curve.
And, uh, on top of that I had to deal with the immigration challenges. But finally I was able to get my internships and my first job as a data science consultant at a startup. It was called PharmaAce. And that is where I was able to work on some of the impactful use cases, uh, such as predicting the number of drugs that will be required for rare disease patients. So it was a life and death kind of, uh, a prediction problem. And from there I realized that the importance of data is much more in the AI world. I always say data science is limited to ".fit", ".predict", but if you really need to get to the weeds of things, you need to understand the data.
So I decided to take more data driven roles and that is how I decided to go to ZS Associates, which was another pharma consulting firm. And from there I was able to pivot to financial sector, to Morgan Stanley, though healthcare and finance seem like two different sectors, they still share the same kind of regulation.
So if you learn to adopt to one of the regulated culture, you can easily use those transferable skills, uh, to another regulated industry, which is finance.
[00:03:51] Lauren Burke: That's awesome. I definitely feel as someone in healthcare, in data in healthcare, the regulation piece of that, there are so many things you have to be thinking about and following and it is kind of shocking when you're going into that. But it's nice that you mentioned as well that you can take those skills and take that to any other industry, any other role that'll operate in a similar way.
[00:04:13] Supreet Kaur: Yes, definitely.
[00:04:15] Lauren Burke: That's awesome. And so I know you've mentioned data strategy before, and so you're an AVP and with that you have a hand in defining the data strategy around AI products. So what do you think the role of data strategy is in building a data-driven culture?
[00:04:33] Supreet Kaur: Exactly. I think all of this, uh, started stemming out of the fact when people started talking about data-centric AI, data-driven culture. Uh, such roles started coming up. I remember when I was applying for jobs in 2019, data strategist didn't exist. It was barely a role. And in the past three years, I've observed that industry has pivoted and realized, uh, that these specialized roles are needed not only, uh, to serve as who would be your strategic partner when you are building those AI products, but also who would be managing those AI products once they are in production? Because your work just doesn't stop. Once you launch the product. You continuously need to monitor it. You need to think about simulations, you need to think about, uh, I would say creative strategies to test your product.
Okay. Am I still increasing the customer engagement. Am I still creating that X percentage of impact that I was supposed to create? So someone needs to answer those questions, and I think that is where a role of a data strategist, uh, stamps out of that need. So, yeah, I think that is pretty much, you know, the day-to-day of a data strategist.
I know that some data strategists also handle the data governance aspect of it. Um, though in Morgan Stanley it's a little different, but I've heard from other organizations that even that part is included in data strategy.
[00:06:02] Lauren Burke: I completely agree. Just a few years ago would, I never saw anyone with a title data strategist, but people were doing data strategy. You mentioned data governance, which is a lot of the times where data strategy lied pretty much solely for a company and now you're seeing it start to branch out and be more embedded in different areas. Which is really cool because with data, without that intention, without that strategy behind it, without that lens, you just have a lot of data. You're not really sure where you're going with it. So I think that's great that you're starting to see, and we're starting to see more people in roles that are really focused on defining that and setting the kind of guidelines and the direction we're going in.
[00:06:45] Supreet Kaur: Yeah.
[00:06:46] Lauren Burke: So kind of going back to the data driven culture part of that, that is something that really defines how well your company is going to succeed in what you're doing with data.
So throughout your time you worked as a data science consultant. In your current role where you're an AVP. What are some of the other ways that you feel like you've seen data-driven culture being built up and being built up successfully?
[00:07:09] Supreet Kaur: Yeah, I think there are many ways an organization can pivot to a data-driven culture, right? One need obviously stems out of the fact when people want to build a AI product and then they realize that the data's scattered. I don't have a database, I don't have, uh, a cloud technology to maintain it. I don't have those connections to maybe, let's say AWS or cloud platforms, um, and which kind of can hamper your training journey or, you know, leading the model to production. So I think that is when they start realizing the need for building a data-driven culture.
So, just my personal thoughts are right, like if you are trying to build a data-driven culture, number one is obviously to think about do you want to keep your data on prep? Do you want to migrate it to cloud? What would your use case look like?
The second thing is about educating folks about the data, right? There is a repository of data set, but I know when people join the company, they don't know what it means, right? So there's a lot of education involved around that as well.
And the third aspect is how do you look at the access issue? Because data privacy is another important aspect of this data-driven culture. There are some PII. There is attributes that everyone shouldn't see. You don't need to see SSNs of people. So how do you build a regulation around that PII without tampering, uh, your organization's AI journey?
So I feel like those three parts are essential for this.
[00:08:51] Lauren Burke: Yeah, I agree. And you mentioned something that'll kind of lead us into our next topic. You mentioned the regulations and privacy, which also kind of aligns with the trust element. And when you're getting into developing AI tools, even just using AI-based products, you have to look at that a little bit differently.
And so now we're hearing a lot about responsible AI. So in your opinion, what do you think responsible AI looks like in today's pretty rapidly changing landscape?
[00:09:20] Supreet Kaur: Yeah, I think terms like responsible AI and explainable AI are often thrown interchangeably and now they are becoming, you know, the terminology of everyone. But I think at the end of the day, you just have to understand that explainable AI kind of helps you drive responsible AI. Explainable AI provides you those tools and frameworks so that you can be a responsible AI organization, someone who is cognizant of the fact that with great data comes great responsibility,
And I need to respect the privacy of people, but at the same time, I also have to develop cutting edge technology, cutting edge products so that I can serve the customers and be at the top of my game. Right. So how do you balance that is about responsible AI. Uh, and the second thing is, okay, if I'm using the explainable AI tools, if I'm using those frameworks, uh, do my data scientist, understand those frameworks, which framework is fine for that, right?
And that is where you also can talk about the human-centric AI, where even if your explainable AI is throwing an output, you're not just trusting it blindly. There is a human element. There's someone who's reviewing those results, uh, before it actually goes out to production or senior leadership.
[00:10:42] Lauren Burke: That's great, and I think that's such a great way to differentiate between those. You do need both, but you don't need to approach both in the same way. You need to approach both in different ways, and that combination is what's really going to lead you to that trustworthy AI element, right? The privacy, making sure you're hitting all of the regulations you need to be.
So kind of going back into explainable AI, one of the things that it can help do, and one of its benefits is it can help you establish trust with your users, with leaders, with other stakeholders. What are some of the other benefits of explainable AI and really making sure that's a practice you're taking to heart when you're getting started with AI based tools and AI-based products.
[00:11:23] Supreet Kaur: Yes. Yeah, I think that's a great question, because I feel like explainable is not only essential to explain a senior leadership or a non-technical audience what my black box is doing, but it's also essential for you to see, "okay, what features or what data elements are driving my AI model" because that will help you to get that business knowledge that will help you to see, "okay, how is my customer interacting with, let's say, different products?"Right? Okay. Is the prediction of this X, Y, Z product, depending on this, you know, let's say A, B, C factors.
You need to develop that I would say strategic sense and that business acumen as well. So explainable AI is also important on a personal level. Right, and obviously the second thing that I feel, and this has been my personal experience as well, explainable AI can actually help you detect if there are any mistakes in your model.
There is a possibility that you might have built the model perfectly, but you used deep neutral network, right? And you just kind of trust the model. Okay? Whatever it has done, you know, it might have done right. But then if you have a simple model, you can compare the results, you can compare the driving factors and see if there are any missing pieces to it.
So even that is an important aspect I feel, before your model goes to production.
[00:12:47] Lauren Burke: That's such a good point and such a good thing to be calling out because I feel like when we're thinking about explainable AI, when we're talking about it, we're thinking about it in the context of we are AI practitioners that are trying to explain it to people at a different level, with a different technical expertise, with a different level of understanding. But really you're taking it back and you're saying that the main piece of that should be the understandability. And so understandable AI at every level, starting with yourself is really the core piece and the key to making sure that what you're doing, you're doing successfully, you're doing responsibly, and you're doing in a way that is maintainable.
[00:13:28] Supreet Kaur: Exactly, and I think that is when you can also, you know, challenge the model. And that is another aspect of responsible AI, right? That you're taking complete responsibility because now you understand what exactly happened behind the scenes.
[00:13:42] Lauren Burke: That's great. So you're not just trusting everything, right? You're, you have the capability and the knowledge, and most important the flexibility in what you're doing to know when something is not going right, is biased, and make sure that you're there and you're ready to make those changes, those adjustments that are needed.
[00:14:00] Supreet Kaur: Exactly. Yeah.
[00:14:01] Lauren Burke: That's great. And so around AI, just in general, we're seeing a lot of different developments, a lot of new trends coming out, new innovative tools. So what are you excited about for AI in your current industry, in financial services?
[00:14:16] Supreet Kaur: I am definitely excited and interested to see how GPT will be adopted in the financial industry, considering the regulated aspect of it and how would we mitigate through that, uh, regulated component and still be able to adopt it to serve our customers better. I'm definitely excited and I'm also excited to see, uh, there are a lot of AI tools that are coming in the market and some of them are open source and I use multiple of those.
Some are powered by GPT, but some are powered by other technologies. So, if we can also get out of our Jupyter notebooks and build some great AI products, that is where, uh, not only financial industry, but I think other industries are heading to.
So we would see more models in production and we would see more, uh, AI notebooks converting into tools. And that is a development that I'm excited about.
[00:15:10] Lauren Burke: Okay, so you're kind of talking about the like, low code or no-code solution.
[00:15:14] Supreet Kaur: Exactly.
[00:15:14] Lauren Burke: So really, and that goes back to what, um, I mentioned earlier about you is you are a big fan of democratizing AI, democratizing ML. Um, so just what other ways do you think that we could be going about this and some of these tools, like you mentioned GPT four are already doing that, but what are some of your other thoughts on how we can be better democratizing AI?
[00:15:36] Supreet Kaur: Yes, definitely. And I think the, uh, I would also tie it back. So there are two things that I would like to cover. One is tying back to the data-driven culture, right? When we talk about data-driven culture, we often talk about, okay, this is like the technical component that you need to consider, get the data, ensure that it's right, and do the data quality checks.
But there is another component which is, um, from your analyst. To your director or uh, managing director, is everyone able to crunch the data and get those required insights, you know, in the form? Because maybe you have a dashboard or you have a GPT driven dashboard, or you have given them the right tools so people don't need to code and SQL anymore.
Right. And I think that is also another part of a data driven culture is that everyone has access and that is a step towards data democratization. When everyone is able to crunch the data, get those insights. It might be a marketing person or a business development person, or a data scientist because we have, uh, codes and no code approach for them.
And then the second part I feel is as practitioners, the on is on us to simplify these very complicated topics that we have got the privilege to understand during our career, during our, uh, let's say work. Uh, so, and through LinkedIn, that is what I want to do, right, is okay, I can break these complicated topics for you so that even if you're not a data scientist, you can understand or I can share the required tools for you so that you can learn some of these things.
[00:17:16] Lauren Burke: That's great. I think one of the things that really ties back to that, that's something I had to learn, I feel like many people had to learn going into their first or even later data roles is documentation. The explainability part of that, you need to have user level documentation. You shouldn't be the only one that can understand something you're doing. You should be able to show it to someone who has maybe a different level, different lense on what you're looking at, and they should also be able come away with what they need.
And so that kind of ties into what you've mentioned just now and really not even just explainable AI, but explainable data in the sense of data literacy. Not putting it on the people that we're trying to have gain that data literacy, taking it and kind of taking that initiative as practitioners and allowing them to, allowing that collaboration in a way that everybody wins.
[00:18:08] Supreet Kaur: Exactly. And I think there's, I've also seen this. It might be a biased opinion, but I have observed in data scientists and AI practitioners that, you know, we kind of fall in love with, uh, complexity, but at the end of the day, it's all about simplicity. So we should stick to the basics.
[00:18:27] Lauren Burke: Yeah. Like Occam's razor, right. MVP. Right. Everything.
[00:18:32] Supreet Kaur: Yeah. Mm-hmm.
[00:18:33] Lauren Burke: Yeah, that's, that's one of the hardest things you have to learn is not trying to go for a neural network or a crazy boosting model. Or a ensemble, crazy ensemble thing when just a simple regression will do. And it's hard to get out of that because it is the fun, sexy part of data science, but, right.
I think the, the maturity comes from knowing when you don't need those kind of things and knowing when you can satisfy requirements with the simplicity of a nice regression or something like that.
[00:19:02] Supreet Kaur: Exactly.
[00:19:04] Lauren Burke: That's great. And so kind of speaking of new developments and trends, you have actually created a community to mentor aspiring data enthusiasts and to share the latest trends with that community.
So can you tell us a little bit more about Databuzz and the inspiration and how you got it started?
[00:19:22] Supreet Kaur: Yeah, so Databuzz, I think it's, uh, a baby right now and it's still growing. Uh, in a way the need arose because in 2017 when I kind of started pivoting my career, though, I was in a master's program, a sophisticated master's program, which had. Everything figured out for me that, okay, this is what the syllabus is gonna look like.
I still wanted to do things that were not exactly defined as a data scientist or, you know, MLE. I wanted to do some things which were, let's say in the intersection of data, AI, business knowledge. And I didn't know what those professions looked like back then. I didn't know what those were called.
What should I look for in the market? Right? So that is kind of the goal. To break these complicated terms that you hear about, "okay, what does a data strategist do?" Right? Like, what does a MLE do? What does a AI research scientist do? Now there are so many professions out just stemming out of this data and AI that people kind of feel lost.
And it is okay to feel lost because I was lost once. Um, and just Databuzz, that is the goal, right? And I have seen 13 year old who want to pivot into data science, right? And they're like, okay, we will start doing it. And I've seen 45 year old teachers who are like, okay, we're gonna leave this job and pivot to data science.
So I feel that is, that is the beauty of this community. And by sharing the latest trends, by bringing guests to my, uh, YouTube channel, that is what I want to do. Unleash some of these, some of the potentials of this inclusive space.
[00:21:00] Lauren Burke: That is amazing. Just to start off, I think so many of the things you just mentioned are really key pieces that a lot of people run into. The interdisciplinary aspect that you called out is essential to data science and not just data science, many data careers. Because you're bringing those perspectives that you might not be thinking of a problem in a certain way, but then like the people you mentioned, someone who's transitioning from a career in education, they might be bring a perspective where they're looking at a problem in a completely different way. That you and your team of people who I don't know, maybe coming from even different backgrounds, might not have seen it that way.
So I think that's great. Also just to say, I would've loved that in college because I remember my sophomore year of college not knowing what data science was. Not having even heard of it, but knowing that I loved studying math because of the analytical and the computational, the predictive elements, but not knowing how those pieces fit together and where I could find a career that would also fit those.
So I wish Databuzz existed when, when I was looking for what I could do after college.
[00:22:09] Supreet Kaur: Exactly. Yeah. Yeah. I mean, thank you so much for the support, right? And I think like you, there are so many people who send me messages, and DM me on LinkedIn, so it's definitely, uh, not possible for me to mentor each one of them individually. And that is why I try to share some of those tips on LinkedIn or through Databuzz.
And then also try to answer the individual questions because I understand everyone's journey is unique.
[00:22:33] Lauren Burke: Yeah. And one
of the things you're doing is you're sharing those trends, those new developments in the space. Which is great because a lot of times, like you can't just Google AI, you can't Google data science. You would be immediately overwhelmed and you won't find what's really new and next. You would have to be looking and trying to figure out what specific resources, newsletters, all of these crazy, crazy things.
So it's really nice that for people that are just getting started or interested in seeing what's going on and having a quick view into that, you're being able to be a resource for them just to see a little piece of what's going on and what's next.
[00:23:09] Supreet Kaur: Exactly.
[00:23:11] Lauren Burke: That's awesome. And so along with building that community, you have also done an amazing job of establishing a personal brand. And so how have you used tools like LinkedIn, like you just mentioned, to help with establishing that?
[00:23:25] Supreet Kaur: Yes. I think, uh, that's one thing I regret, right? If someone would've told me earlier the importance of, uh, building a personal brand, I would've started when I was a sophomore, or at least when I was pursuing my master's degree. But in the past two years, I started being active on LinkedIn, going to conferences, uh speaking.
You know, and I think until this point, I delivered some 50 talks. Right? And then when I started chatting with people, I realized that if you have a personal brand, you get more than your job satisfaction, right? You're able to impact people in a way that you never thought of. You are sharing your story, you are out there, you're vulnerable, you're using LinkedIn, or I know that Gen Z would prefer maybe Instagram, uh, but Instagram, TikTok, right?
Any of the social media platforms to amplify your voice, which is essential. And when I started doing, I was not aware how many girls back in India I inspired when I started getting these messages. Uh, right. Like, we want to be what you are. And, you know, I was like, "oh, wow, this is great."
And kind of that inspired me to work on my personal brand. I'm, I'm still trying to, I would say figure it out all, you know what exactly what is my niche and everything, but I feel it's, uh, progress over perfection. So I'm taking those little steps each day to share content for my audience and to share just mine experience on LinkedIn.
[00:25:01] Lauren Burke: That's amazing. And so first of all, I love that people are reaching out and sharing the impact you've had because that's just such a cool feeling to be able to see and especially when you're just getting started. It, it's intimidating.
And the second piece is I feel like personal brand, just because we call it that, makes it so much more intimidating than it needs to be. Like you don't have to be a keynote speaker to start defining what's considered a personal brand. You can be creating a website, you can be crafting your LinkedIn. Basically just so when people find you, when they Google someone in data science with these categories, with these characteristics. When they find your profile, they're able to understand what you're about, what you're interested in, the impact you're having.
And then one of the things you mentioned as well is opportunities come to you. If people are looking at you and they're looking for a speaker for an event, they're looking for a guest on a podcast. They're going to find you, instead of you having to go to them.
And so even if you're not considering it a personal brand, just considering it your personal presence. And defining where you are, where you wanna go, and just sharing that in a way that people who run into you can quickly understand that can really help you, not just in your career, but where you're going to be in your career.
[00:26:16] Supreet Kaur: Yeah. And I think, there's one thing that you touched upon, which I would like to elaborate here, right. More than the impact and also the speaking engagements. The kind of opportunities that I've got on, uh, advising companies. Advising Rutgers and Columbia students. Everything was through LinkedIn, right?
All my speaking engagements as you just mentioned. I also get a chance to chime in on the latest research that's happening in AI. I get featured, uh, right? In magazines and all those things are through LinkedIn. And all my jobs were through LinkedIn, right? So sometimes I hear students saying, but I don't know what I need to share, right?
Like, I'm just a student, but I'm like, you have a journey. You know, let's start with, okay, why are you here? Why are you student? Like, why did you choose this course? Right? There's so many things that you can share, and what is the end goal of a student is to get a job. So maybe in that process you would also end up doing coffee chats with people, meeting new people, and getting a job.
[00:27:21] Lauren Burke: I think the way you phrase that is so, is so good too. Because especially what you called out there about finding opportunities. People are going to be able to find you and bring those to you. So if you're looking, no matter how many you could possibly be looking for in a given day, week, year. Think of all the others that you are not having to look for, that are being brought to your attention because someone found you, because they were able to find you.
That's a lot of effort that you're not having to do, right? You're still having those opportunities because someone knows you're there, someone knows you're interested.
[00:27:55] Supreet Kaur: Right, exactly. And I will also say for anyone who's embarking, you know, on this journey, and if you decide to use any of the social media platforms, you also need to think about not everyone is going to like your content, right? And there is some kind of hate that you might receive, you know. But at the end of the day, you need to remember those good days when people send you those good messages because that is what is going to keep you going, uh, rather than the people, you know, commenting out that, oh, maybe she has a lot of free time and, you know, all people calling out your content.
So just an fyi, it's not all rosey out there.
[00:28:32] Lauren Burke: I, I heard this saying recently that I think is so relevant. That you could be the sweetest, tastiest peach on the tree, but some people just don't like peaches.
LinkedIn that applies, right? You could be producing content that's amazing, that's so helpful and some people just aren't interested. But it's not for them. Do it for the people that it is for and ignore all of the rest.
[00:28:52] Supreet Kaur: Yes, exactly.
[00:28:54] Lauren Burke: That's awesome. And so you mentioned speaking, which is something you have been doing a lot of recently. You've been featured at a lot of conferences, in podcasts, just honestly everywhere. And you're doing an amazing job of it, possibly because people are able to find you. Because of this personal brand you've established.
So how did you get started with speaking and what are your tips for someone who might be looking for their opportunity?
[00:29:18] Supreet Kaur: Yeah, I think that's a great question. How I got started is actually a funny story because, uh, so it was one month into my job at I think it was ZS. Yeah. And one of my friends got the speaking opportunity and he was not able to make it for some reason. And he asked me, "Supreet will you be able to cover for me?"
And I was like, mm, I've not done this before, but okay, let's try. I mean, I was obviously always a person who was on stage in school. I was a lot into acting. But then my career happened and I was too much into math and science and it kind of, you know, got blown away.
But, I think that is when I went back to the stage and there was something about that energy that kind of re-energized me. Right. Now, even when I'm flying, taking red eye flights and maybe working on the flights or at the airports, and sometimes I'm traveling internationally for these, I just don't feel tired, right? Because I feed out of that energy. I feed out of the energy of my audience. So, how I got started is simply because of that.
And then, uh, I was hesitant to share it on LinkedIn and for one year I never shared any of this on LinkedIn because I was scared. Oh my God, what will my people think about it? What will my employer think about it? Will he or she think that I have a lot of time? Will he think that I'm free? Right?
So all of those doubts and I, I stopped sharing it. But then from 2022, I decided, I'm finally going to do it. I'm going to share each and every opportunity so that people know, right? And they can also ask me, they can meet me at the conferences.
And that is when the entire journey started. Most of my speaking engagements are because I develop connections. I go to one of the conferences and then they call me in the next. Or someone suggests my name, and then they reach out to me through LinkedIn. And obviously there are a few where, you know, they reach out to me through LinkedIn directly. So I would say at the end of the day, it's just about putting yourself out there, right?
And if you need the first gig, contact me. I know all of these conference producers and I'm sure I can hook you up with someone who can get you started.
[00:31:33] Lauren Burke: That's great. And can I say what a great form of feedback to get that people saw you at a conference and asked you to speak at their next event or recommended you. That's such a good form of recognition, right? And that, I'm sure that's another piece of how, how it keeps you going, right?
Because you're like, ah, I know what I'm doing. Someone is benefiting, someone is enjoying that. Someone is interested in me continuing this and having this impact on another level in another area, and just keeping that going forward.
[00:32:03] Supreet Kaur: Exactly. And I think Lauren, I am glad you mentioned this because uh, you know, some people think that okay, you know, I just had some natural instinct and natural talent that I could go on a stage and talk in front of 300, 500,000 people and it was not that. Right? You are still an imposter. You know, you are still going to have those feelings that I'm not good enough or someone's gonna find out, or maybe I'm a fraud, right?
But these kind of validations kind of help you going that, okay, maybe I'm doing something right, right? That people are reaching out to me. There is something, right. Maybe I'm not perfect. But there is something right about me, so it's, I just want that message to go across, especially to the women who are listening to this that, you know, you're not a fraud.
[00:32:49] Lauren Burke: I absolutely agree with that. That is such a good point. And I . Think it, it just speaks to where you're starting, right? You don't have to start with keynoting massive events. You can start on a podcast, on a webinar. You can start just having a chat with someone and putting it out there for other people to be seeing and commenting on.
And then as you get more comfortable, as you get that feedback that what you're doing is helpful. It's impactful. It's bringing encouragement to other people in those roles. So you don't have to start big. You can start small and work your way up.
[00:33:20] Supreet Kaur: Exactly.
[00:33:21] Lauren Burke: That's awesome. And so our last question that I like to ask everyone is, what is a resource that has helped you in your career that you think might help others?
[00:33:33] Supreet Kaur: In different parts of my career, I would say the resources have also evolved over time. When I was just starting out data science, I used to rely a lot on, uh, blogs, right? And I always used to type, uh, XYZ concept and explain like I'm five, right? So that I can understand not only the nitty gritties of the concept mathematically, but also if someone asks me to explain that topic, I'm able to do it like an evangelist, right. And I'm able to do it flawlessly. So I think that is one thing that has helped me.
Then the second thing was connections and my peers. Even during my master's degree, I used to rely a lot on people who have been in the industry for a few years and then decide to pursue masters .Because I just came directly from college, so I had no idea what a data scientist does or what a data analyst looks like. But I was able to rely a lot on them and ask them, okay, what did you do? Right? And what, what skills do I exactly need to, let's say ace the interview or ace this position? So now the other thing is rely on your peers. Right? Find that support group that is very important.
And I would say now, for me, it's more about conferences. If I go there, if I'm speaking, I'm also absorbing some content, right? When you're speaking it's a two-way street. First of all, I'm researching topics that I wouldn't have researched otherwise. I'm creating presentations for my audience, but at the same time, I'm sitting there and absorbing content from other leaders. That is a great resource.
And the last resource that I would like to mention is, LinkedIn. If you follow the right creators, you can absorb information. You might just get bite size information, so you can always make a note of, okay, this is a topic of interest. I want to dive deep into it, and then, uh, learn about it more offline.
[00:35:32] Lauren Burke: That's great. So on LinkedIn, who are a few of your favorite creators to follow?
[00:35:37] Supreet Kaur: Yeah, sure. I think, uh, obviously Allie K. Miller for AI products, and then I also follow Smriti Mishra. She's great. I follow Isha Rani, um, she's in Microsoft and Serena, who is from PayPal, so some of them are, yeah, they're awesome.
[00:35:58] Lauren Burke: That's great. Well, I will link all of them in our notes section because I agree everyone should be following people on LinkedIn who are leading these innovations because that's how you're going to be finding out what's next.
[00:36:10] Supreet Kaur: Exactly.
[00:36:11] Lauren Burke: Awesome. And so how can our listeners keep up with you?
[00:36:15] Supreet Kaur: Yeah. I think LinkedIn would be my answer. Uh, right. Please feel free to follow me. DM me on LinkedIn. I think that's one platform I check more than WhatsApp messaging, Instagram. So you can definitely DM me and expect a response as soon as possible. And the other thing is you can also look at some of my Medium blogs.
I also tend to write not only about my professional journey, but also about my personal fitness journey, um, and how I felt when I landed in the United States.
[00:36:47] Lauren Burke: That's great. Well, thank you so much for joining me today, Supreet. This was such a fun conversation and I am so excited for others to listen and learn a little bit more about some of the awesome things we discussed today.
[00:36:59] Supreet Kaur: Yeah, thanks Lauren. I'm looking forward to the recording and also seeing what people think about this.
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