Chhavi Arora: [00:00:01] I take a lot of pride in how passionate I am about why I want to be a data scientist, about everything I do, being a data scientist now, and I just want to make sure that everyone all hears my story. Knows that passion is the only way to go. And if you're passionate, you would invest your all your time and energy into doing.

Harpreet Sahota: [00:00:36] What's up, everyone? Welcome to another episode of the already some data science. Be sure to follow the show on Instagram at the Artist of Data Science and on Twitter at Artists of Data. I'll be sharing awesome tips and wisdom on data science, as well as clips from the show during the Free Open Mass, My Slack Channel by going to bitly.com/artistsofdatascience. I'll keep you updated on biweekly open office hours. I'll be hosting for the community. I'm your host, Harpreet Sahota Let's ride this beat out into another awesome episode.

Harpreet Sahota: [00:01:23] What's up, artist? Welcome to a very special. Rising Stars edition of the Artists of Data Science podcast.

Harpreet Sahota: [00:01:28] During this special episode, we'll take an opportunity to speak with one of the rising stars of our industry to get an insight into how she broke into the field, the hurdles she had to overcome in the job search, and how she answered commonly asked. Behavioral interview questions. Our guest today has a deep and profound love interest with data science. She's crafted a few end and data science projects in her portfolio and is constantly working on enriching her knowledge in the many aspects of data science. During her undergrad years, she got an opportunity to volunteer for an NGO where children with special needs performed live for a cultural event. This experience helped her realize that there are many hardships that people face each day, whether it's personally or professionally. This experience left her feeling that she could find peace if she was in a career that involves making an impact in the lives of people in her community and beyond. Is with the guidance of her statistics, professor, that led to the discovery of the wonderful world of data science and machine learning and the appreciation of the mathematics which forms the backbone of our art. She's earned an undergraduate degree in mathematics, statistics and computer science from the St. Francis College for Women and a Masters in Mathematics and the Christ University Bangalore. She's continually developing herself here online courses to become acquainted with the technical aspects of their science and apply them through a few. And to end open source projects, quite fittingly, for her personality. She's currently employed as a data scientist at Achievers, a company that leverages the science behind behavior change so people and organizations can experience sustainable, data driven business results. So please help me welcoming our guest today. Someone I refer to as my M.E.. Aurora HIV. Thank you so much for taking time at your schedule to be here today. I really appreciate you joining me on the podcast.

Chhavi Arora: [00:03:11] Absolutely. My pleasure. Actually, an honor.

Harpreet Sahota: [00:03:14] Talk to me about the experience at the NGO and how that got you interested in data science and and machine learning.

Chhavi Arora: [00:03:22] Yes. So during my undergrad, as you mentioned correctly, I used to volunteer a lot for a bunch of NGOs, but there was a specific one which catered to a bunch of children with special needs. And they used to conduct different events, social events for them and the students there with special needs, they were the ones who need to step up and perform. And I mean, it just baffled me to see that there's so many things and go around and around us and the world beat on personal level, be on a professional level and so many questions. People want answers for. And I felt I just internally I had this I feel like I found my calling and I felt I would find peace eventually in a career where I can impact the lives of people I work with or who might come in contact with everyday in in one way or another. And coincidentally, it was during my undergrad to statistic professor has been in touch with me for just understanding a lot. And he he in fact introduced data science to me. And that is that as a first time, I heard of the whole idea of machine learning and how it has been used in a variety of formats to help people around. And I just realized that this is this is the career I wanted to build for myself. And that is when I decided to do a masters in mathematics, because mathematics is the foundation for machine learning, no matter what, no matter how much you code, it is the essence of it. So I just wanted to have a very strong foundation to begin with. And that is that is the whole idea of why I eventually decided to be a data scientist. So it goes all all the way back there.

Harpreet Sahota: [00:05:07] That's awesome. Yeah, definitely. Mathematics is for sure. Language of the universe. And you've had some really awesome projects. We've got an opportunity to apply everything that you've learned. Can you tell me more about how you went about building out your projects? How did you come up with ideas?

Harpreet Sahota: [00:05:32] Are you an aspiring data scientist struggling to break into the field? Well, then check out dsdj.co/artists to reserve your spot for a free informational webinar on how you can break into the field. That's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out, dsdj.co/artists

Chhavi Arora: [00:05:57] Honestly, when I started doing projects, the first one was something that I learned from DSDJ (data science dream job) itself and the first project I did. I didn't want to venture out. I'm always secure person like that. I just wanted to try something now that there has been done before. So I tried to use the guidance of my mentors and DSDJ and do a project that was in there as guided by the mentors itself and with the understanding of how the whole approach goes. I decided to pick my next project in the similar way. Honestly, I would have to specify this, that my next project was something that came out of you because you were the one who spoke to me about how that how every project you do as a data scientist needs to be something that you have interest in so that you know what questions you are looking for and you eventually find answers to your work. So... the fact that I was also constantly looking for drugs at the same time, every company I used to interact with, I in my in my head, I used to feel that when I'm looking for a job and when I'm interviewing for a one particular company, I become highly invested them. And the more I read about the company, the more I realize that I would want to do projects related to them so that when I actually do get a chance to interview them in person.

Chhavi Arora: [00:07:17] That was that would be the best position for me to talk about. So every every other project after my first one was something related to that. I deeply attached myself to be the companies I interview with specifically. And the whole idea about working on a project was that the first few days, literally, even if I get an assignment from an interview to my first two days. All I do is sit down, take a pen and paper and think about all the things of why I'm doing this. Because in my opinion on the question, do you frame on that first day thinking through the idea of your project becomes the hypothesis you end up looking for. And quite a great amount of investment in that area gives you a lot of foundation for your project. And eventually that also becomes a good support in the end when you're actually talking about your project and explaining of why you did this in the first place. Because when you have those questions in the beginning, you see you walked through them. You get some answers to there. And in the end, you can talk about that. I did this to answer these questions and these questions have this impact on your project or whatever you're trying to achieve. So, yeah...

Harpreet Sahota: [00:08:27]  That's a really good approach, right?

Harpreet Sahota: [00:08:29] No, no, that's perfect. Detail is good because there's a lot of aspiring data scientists out there who are, you know, definitely gonna learn a lot from from your journey.

Harpreet Sahota: [00:08:37] And I think that's a really good approach to take that innate interest in a project that you're building out, because it really is going to show in the quality of the work that you do for the project.

Harpreet Sahota: [00:08:54] What's up, artists? Be sure to join the free, open mastermind slack community by going to bitly.com/artistsofdatascience.

Harpreet Sahota: [00:09:02] It's a great environment for us to talk all things data science, to learn together, to grow together. And I'll also keep you updated on the open biweekly office hours, and I'll be hosting for our community. Check out the show on Instagram @theartistsofdatascience. Follow us on Twitter @ArtistsOfData. Look forward to seeing you are there.

Harpreet Sahota: [00:09:23] How important is having the right mindset during the job search?

Chhavi Arora: [00:09:29] It's it's the most important thing ever. Honestly speaking, when I started the DSDJ and that was the major change that I learned from the DSDJ was the growth mindset. And I've been trying to apply that ever since. So even with the job search, because of how much competition we have now, it's really difficult to get through the door if you don't have the right mindset. And when you do have that mindset, you will not fear rejections, you will not fear the amount of energy and investment you need to make to get there. And you don't fear it, because if you don't have that mindset and every step you feel like, OK, because it's not easy. Machine learning is not easy to solve any aspects of design that are not easy. And to actually make your way there, you have to have the right mindset. It's the most important thing. So I would suggest that whoever is trying to look to become a data scientist, eventually the first, first and foremost needs to be something to get your mindset Right, because that is going to make your way.

Harpreet Sahota: [00:10:28] So, yeah, definitely, because there's so much we have to learn through our careers as a data scientist that if you if you come from a from the type of mindset where you think that you can't learn anything or because you're not smart enough, you're not, you know, don't have the right degree or what not, then you really holding yourself back right, so adopt that growth mindset, believe that you can learn anything and you'll just be able to have an amazing career as a data scientist. So what would you say was kind of the biggest self limiting belief that you had to overcome when you were in the job search?

Chhavi Arora: [00:11:04] For me personally, the most crucial aspect for my career was my one year long gap. Essentially I completed my master's degree, I worked for a company for a bunch of months, and then I got married and moved to US with no visa. And so I struggled for a year, not having the visa. And for a very, very long time, I felt this is my major roadblock. This is the this is going to be the end of my career. So coming out of that mindset was the hardest for me, because I realized at a point that because of that mindset, I, I honestly had a roadblock and everything when I was trying to learn a new concept. After trying two or three times the fact that I didn't have the confidence in me, it was a roadblock. So every new door that I went knocking on, it didn't work out because I was so stuck in myself when I was so insecure about the whole thing that it just didn't work out. So the whole idea about my journey was the major roadblock was to get or what my inhibitions a little bit and to make sure that I have confidence in state and myself that I can do it.

Harpreet Sahota: [00:12:14] How did you address those resumé gaps during the interview process?

Chhavi Arora: [00:12:19] Oh, interesting question, sir. I vividly remember my first call with Kyle after I was done reviving my resume and I spoke to him about it with intense pain. I spoke to him that I had this one year long gap and I don't think that I want to get a job again. This is going to be a question that everybody would bring in. I still remember what he taught me that day. He said that every little weakness that you think you have can become a positive thing if you spin the story right. And I realized that makes so much sense. And he gave me a story to begin with. He said that I think you should be proud about what you accomplished using that one year gap and I utilize the same concept there. And honestly speaking, till that day, I. I thought that this is one area of my journey that I do want to bring up in any of my interviews. And I felt like it would be great if nobody noticed it. But from that point onwards, every person, every interview I, I went through.

Chhavi Arora: [00:13:24] I made sure that I speak about that one year gap with a lot of pride. And I mentioned specifically that this one year gap was was the winning point for me because I utilized my passion for data science and I use it for the benefit of my own career. I had worked on a lot of projects and I did everything I could and the fact that I didn't have a visa, or a job for that one year did not bog me down at all. And I further utilize it for my future eventually. So I would say that every little gap that you worry about be it a time line, or be it anything that you think you have, be it on your Academic foundation or whatever, treat that as your superpower. Honestly, I take pride in your decisions and whatever has happened with you, and it will show honestly once you take pride in it. The interviews will see through it and they'll be like Okay. Looks like that's a good thing for you. 

Harpreet Sahota: [00:14:16] So, yeah. That is some excellent advice yeah because I know there's a lot of people out there who are in between jobs, but they are using this time wisely to upskill, to build their portfolios, to build their skills set up.

Harpreet Sahota: [00:14:26] And they automatically think that it's a negative thing. All you have to say is like, yeah, there are some circumstances that led to me being unemployed for a certain period of time. But here's what I did during that time. That's helped me become more valuable to any potential job that I go for.

Harpreet Sahota: [00:14:42] So that's a great way to frame that. Now, if you don't mind, I wanted to get into what the job search process was like for you walking through your process for applying for jobs and then getting interviews or whatnot. So did you just send a resume and just hope that somebody would call you back?

Chhavi Arora: [00:14:58] No. Absolutely never. It has never worked out that way, honestly. I read a bunch of posts on LinkedIn complaining about how ATS system suchs, and how there's so much competition you're just never going to make your way through. In my belief that there's no point in criticizing the way things are. Because of the amount of competition. Rather take it to your benefit and leverage the idea that because we are so advanced at the moment, we have means and ways to actually get to the hiring manager. Directly and honestly, every interview that I secured was through network networking. That was the only way that was. But isn't that isn't that a good thing? I mean, in my understanding, actually, speaking to the hiring manager, directly impressing him even before the interview process begins is like a brownie point. And the interview process rather than going through the traditional route, getting your interview picked, someone contacting you. I feel, you know, you'll miss out on the brownie point completely. So it's a it's a good thing now. And networking is the way to go. The more you invest your time there...

Harpreet Sahota: [00:16:03] Because you already had that, like, relationship established before you...

Chhavi Arora: [00:16:06] Exactly.

Harpreet Sahota: [00:16:06] Turn up for the interview. So how many interviews did you go on before landing your current role?

Chhavi Arora: [00:16:12] I must have applied in network for a 50 to 60 roles. But I interviewed for, say, 20, which which means that there were a lot of objections on the record.

Harpreet Sahota: [00:16:24] What was your what was your timeframe that day?

Chhavi Arora: [00:16:28] I was lucky because I was not working on the side. So this was my whole focus. So it took me around two to three months to land a job since I started looking for one. But timeline doesn't matter because every person will have a different learning, different perspective, different circumstances. And so I would say that I don't think anybody should learn from how much time it took for me to get a job and compared themselves to it, because there are a bunch of different things I was not working at the time.

Harpreet Sahota: [00:17:02] So that's this awesome advice, yeah you're right. Don't compare your chapter one to somebody else's Chapter 10. Do you have any words of advice or encouragement for those rising stars out there who are now in the same position that you once were?

Chhavi Arora: [00:17:16] Yes, the most most important thing is to never, never stop being passionate about data science. If the only reason why you want to be a data scientist is because of the glamorous title or because of the privileges that come along with it, it will be very difficult. Honestly speaking, because of the hardships you go through to get there, you would need immense passion and true, true, immense passion to get there. So the first is to make sure you take sometime and understand and do some retrospection to make sure that this is your calling and you are super passionate because you're learning is never gonna stop. Getting a data science job is not the end of the world. They're going to be so many other things after that. And every day is going to be a new learning. And if you don't enjoy it, there's no way you can ever be good data scientist. That is my most important advice.

Harpreet Sahota: [00:18:05] Yes, that true. It's definitely a career path for anyone who is committed to lifelong learning because it never is. And it's not even necessarily just technical skills that you're learning lifelong. You've also got to pick up those interpersonal, you know, personal growth type skills as well. So if you don't mind, I want to dive into a few of the common questions that are typically asked in an interview. I know that we kind of covered the tell me about yourself, you know, in the introduction. And you're as you're speaking. But let's pretend now that we're kind of in an interview setting. How would you answer this question? Tell me about yourself.

Chhavi Arora: [00:18:38] Right. So I honestly always begin this term by talking about my superpower. So my biggest superpower is my determination and a desire to learn. I do not shy away from learning new things, investing my energy into diving into a new topic altogether and quite inquisitive in nature. And I enjoy solving problems to make an impact in the lives of people around me, be it internally with the people I work with are externally the customer as me. So I have a master's degree in mathematics and a bachelors degree as a triple honours in mathematics, statistics, and computer science, which helps me build a strong foundation in programming, statistics, and mathematics, which eventually leads up to machine learning. I have always had profound passion in data science and have been working in building that foundation with academics and personal learning and projects. I do try to keep adding new tools to my head data science toolkit with different books and courses and all of that.

Chhavi Arora: [00:19:40] Yes, that's about me.

Harpreet Sahota: [00:19:42] So can you describe a time when you had to deal with competing priorities and with competing deadlines? And how did you handle that?

Chhavi Arora: [00:19:51] Right. So my situation there was I was an associate in my previous job and I was working on a project. I kind of committed to a two week deadline for that. But after a week working into it, I was given a project and project that number two was the deadline for that was as soon as possible. So there was nothing fixed. There are purely defined. So the next task for me was to make a to do list, to define specific tasks that would come under each of the projects. I then spoke to the stakeholders for each of the projects and to try to understand the impact of both of them. And I realized I got to know with those conversations and a project I would do with a flexible deadline was a customer centric project. And the first one that I was working on initially was an entirely internal facing one. In discussing that impact on the board with the project, I decided to do the second one first, which was a customer centric one and followed by the first one. I immediately informed my manager and he rescheduled the first one to a new deadline. It eventually the result was that I delivered the customer centric one - which led to impressing the client a lot - and delivered the first one few days before the deadline was set. So win-win.

Harpreet Sahota: [00:21:10] What's the most difficult type of person to deal with and how do you deal with them?

Chhavi Arora: [00:21:15] Right. I think the most difficult type of person to deal with are the people who are a little adamant in their choice of approach and who take it with a little difficulty if when they suggested a new sort of route to take on task.

Chhavi Arora: [00:21:34] And we do - I'm sure all of us to meet such people along with along the way. And I remember when I was interviewing, I did not have a lot of experience backing me up. So I have a story from my academics. I was working on a statistics project in my bachelor's degree. Two of my fellow students and one was my professor, who was a lot of us. So once we it was basically a project on defining product placement and in the movies and all of that and the importance of that. So we were trying to do an analysis there. My professor there had to suggest an approach and he declared that we should only look for responses within the colleges that I was working in. In my understanding the approach there should have been to do a stratified sampling and to actually reach out a bit more, because eventually the analysis that he was supposed to do, was to define for the entire population of the city and define and getting just a sample from my college would lead to some bias. So my next step was to actually set up a meeting with my professor in a separate setting, in a private setting. I spoke to him about my own inhibitions about his idea. I explored and gave him a detailed understanding as to why, what we want to achieve, and why my approach would make us aligned to that, that detail. And he kind of look through my perspective, and he understood that and he agreed to it. Eventually, he became he decided to make me the lead project something academic title. Well, that's about it.

Harpreet Sahota: [00:23:08] So walk me through your discovery process when you're starting a new project.

Chhavi Arora: [00:23:13] Right. So when I saw a new project, as I mentioned, that I do literally do take a pen and paper and start working on the questions I have. My first set of questions are always about why am I doing this? What is important about that project? My second set questions are what sort of questions I'm actually trying to solve out of this project. And then my third set of questions are what sort of impact this project would bring in the lives of the business in question. For example, I'm sure that a lot of projects that we work on have can have impact on a variety of areas in the business. I just, I just like to list them down so that when I have - this is this entire piece also helps me do the project work. And eventually when I present my my insights and my learnings from my project to the stakeholders, it's a lot easier when I have those questions in place, because then the only way that I display the answers is that these are my questions. This is the impact and these are answers. So I just try to connect the flowchart there. And that is how I deal with the project

Harpreet Sahota: [00:24:20] Excellent, excellent job answering those questions, I like how every question you kind of answer in a formulaic type of way, right? Kind of using that, that STAR format, right. And you can hear it in your response. You use it kind of like a guidepost like you always lead with - There's a situation when A, B and C happened. My task was to do X, Y and Z, the actions that took I took were one, two, three. And as a result, I saw X. Right. So that's awesome formula. And then very, very well done.

Harpreet Sahota: [00:24:55] So what is your process for when it's - Well, there's always that question at the end of the interview. Do you have any questions for us? So what's the process for coming up with questions to ask during the interview?

Chhavi Arora: [00:25:08] It's it's amazing. So the first step, because every first interview is supposed to be an HR interview. And although before you even step in for an HR interview. I always try to literally go to everything on the website of the company if they have 500 articles in their blog. I tried to read at least a brief understanding of all of them because I want to know everything about what they do. Everything because the articles they post on the blog are literally the things they care about. So those are everything I try to know about the company from what is publicly available is my first job. My second step is to actually go into that HR interview and ask a ton of questions to the interviewer because she would not judge me for my technical capabilities, per say. But she would be the best person to answer those questions for me because she knows the internal. So now that I have my piece from what I know from the website plus the piece that she just gave me.

Chhavi Arora: [00:26:05] I work on my questions for my interview with the technical manager or data scientist or someone technical. And my next set of questions come from. Does that analysis that I just did. And every time I speak to our interviewee, I write all of those questions down from everything that I spoke to her. So now that I know everything about the company, that is what they care about, why they are looking for this role. And then eventually I now can fit that into one piece. And I ask questions specific to those concerns they have. So now that I know what they care about, I can try framing questions from a data science perspective. I tried to fit in that if I'm a data scientist already working in that company in role, now that I know I they care about what would be my approach and what would be the things that I would be able to do using my skills when I think about in that direction. I try to have some questions in there on its own and I tried to pitch those ideas to them and I ask them questions on those ideas. I explicitly mentioned that I'm sure that these things you might already be doing already. And if you're not doing, why? Why would this not be something on your radar at the moment? And just it just becomes a very nice conversation.

Harpreet Sahota: [00:27:11] That's very, very good, because people don't really appreciate the fact that an interview really is a two way street, right? It is a conversation. So the worst thing you could possibly do for yourself when going into the interview is when they ask, do you have any questions? And you don't have any. So doing what you do, take an interest in the company, taking time to read their blogs is going to make you stand out from any other candidate that is out there. Let's say it comes time to talk about a technical question and the interviewer is asking you about some technical topic. How do you handle that type of question?

Chhavi Arora: [00:27:46] Yes. So I think when they ask you a specific technical questions, which are not based on any scenario. For example, if they ask you define linear regression or something like that, it would be really, really, really beneficial if you, instead of using your past projects as a foundation to answering those questions as examples. It would be great if you answer those questions or second questions based on the company interviewing with instead. It would just show that you care for that company and you're actually interested because you're putting those technical concepts in the picture of that company. It would be great and that would definitely make you stand out.

Harpreet Sahota: [00:28:26] And that would take a little bit of just researching first about the industry that the company is in.

Harpreet Sahota: [00:28:30] Being aware of the type of problems that they're facing, maybe doing the legwork of reading up on some case studies or whatever, so that, you know, if they do ask about linear regression for whatever you go. Well, you know, I know that in this particular industry, you guys face these type of challenges. Here's a possible solution for that.

Harpreet Sahota: [00:28:49] For a challenge. Yeah, that's awesome. Great advice. One more question before you jump into a lightning round here. What's the one thing you want people to learn from your story?

Chhavi Arora: [00:28:58] The most important thing about my story is my passion. I take a lot of pride in how passionate I am about why I want to be a data scientist, about everything I do, being a data scientist now. And I just want to make sure that everyone all hears my story. Knows that passion is the only way to go. And if you're passionate, you would invest your all your time and energy into doing that. And that's about it.

Harpreet Sahota: [00:29:24] All right. So let's jump into a quick lightning round here. Python or R?

Chhavi Arora: [00:29:29] Python

Harpreet Sahota: [00:29:29] All right. Where do you see yourself in five years?

Chhavi Arora: [00:29:33] I want to be a Data Science Manager. I'm ambitious.

Harpreet Sahota: [00:29:38] What's the best advice you've ever received?

Chhavi Arora: [00:29:41] Best advice. Best advice is by Harpreet himself. Most of my advice is by Harpreet 

Harpreet Sahota: [00:29:49] Too kind, too kind. 

Chhavi Arora: [00:29:49] The best of them is that I need to have faith in myself, which I end up losing a bunch of times.

Chhavi Arora: [00:29:55] And he just keeps reminding me that I am amazing and I need to have that faith that I can do it.

Harpreet Sahota: [00:30:01] Yeah, faith has got to be greater than your fear, right? If you can go back in time to have a conversation with 18 year old Chhavi, what would it be? What would you tell her?

Chhavi Arora: [00:30:10] I would tell her. Please, please, Don't, Don't worry. You are in - The fact that I had this strong dream when I was 18 to be a data scientist eventually and 18 was the time when I decided that in five years I wanna be a data scientist. I just want to tell her be happy, it's going to happen.

Harpreet Sahota: [00:30:28] So how about your favorite book, fiction or non-fiction or both of you, if you'd like, and your biggest takeaway from them?

Chhavi Arora: [00:30:36] Yes. So I've been reading the book called Made to Stick. And it's a book on communication. And as in the whole idea about the book is that every idea that your pitch-in, sticks. So the biggest takeaway is I haven't finished the book yet. But the biggest takeaway from me from that book is that a lot of things that you actually produce, it matters the way you say it and the audience that you're talking to. It matters a lot that you know them beforehand. And you understand that the way the audience behaves is the way you should present them to us.

Harpreet Sahota: [00:31:12] By the Heath brothers. Chip Heath and Dan Heath. 

Chhavi Arora: [00:31:14] Yeah. Yes.

Harpreet Sahota: [00:31:15] And I think they they take the concept of stickiness from Malcolm Gladwell book and just kind of delve deeper into that, Right?

Chhavi Arora: [00:31:22] Yes.

Harpreet Sahota: [00:31:23] That's a great book. Where can people find you? How can they connect with you?

Chhavi Arora: [00:31:29] I am on LinkedIn. Then I try to answer every one who reaches out to me. Unless of course they're not a good way of networking. Nothing like that. I try to as long as people who reach out to me and want to learn I'm more than happy to investment in my time there.

Harpreet Sahota: [00:31:47] So is there is there an effective way people should communicate on LinkedIn? I know sometimes I just get messages and they just say, "Hi."

Chhavi Arora: [00:31:52] I know, it sucks.

Chhavi Arora: [00:31:56] Honestly, in my understanding, there's just so many things I can talk about networking now that I have, because I tried a bunch of approaches that some worked and some didn't work. And the best thing that worked for me is to make sure that your first message to a person is about them, not about you. Please don't talk about how amazing you are, how many skills you have, how many, how much education you have. I can see that on your profile. So my first message that would catch my attention would be to talk about me, about how what what do you learn? What do you learn by looking at my profile.

Harpreet Sahota: [00:32:28] Awesome Chhavi. Thank you. Thank you so, so much for taking time out of your schedule. I know that whoever is listening is going. I learn a ton of this conversation. So thank you for being so generous with your time.

Chhavi Arora: [00:32:39] No worries. And I'm super, super honored. And everyone who is listening to this should know that everything that I am today is because of Harpreet, so thank you.