https://youtu.be/0k6OrCkAEdc Transcript Darshan Doshi (00:08) Welcome to Dasar. I'm Darshan and we have an awesome podcast today. I have with me my close friend Rohit Pandharkar. A quick introduction of Rohit, Rohit is the Global Head of Data Science at OLX Autos. Previously he was the Head of Data Science at Mahindra Group and then also involved with one of the fastest growing startups out of India and the US. He's an MIT Media Lab alumni and has been working in the artificial intelligence, data science, machine learning space for a very long time. He has huge number of use cases where he has helped solve problems, business problems, life problems, customer problems through the use of this technology. So we have with us someone who is in the trenches working, building data science teams, solving critical problems, but at the same time has an exposure to various technologies. And in this podcast we're going to cover a lot of topics around AI. What is AI? What is the difference between AI and Analytics? How do you hire data science teams? What kind of representation is needed of AI within large corporations in the country or around the world? We're going to talk about how AI can improve human performance and make us super humans, if I may call them. Lastly, we also cover the topic of Singularity and we'll get insights from Rohit on how will AI converge into Singularity. So stick in. This is a very long podcast. It is a fast moving podcast. I hope you like it. If you like it, please share it in your network. So let's get started. Welcome Rohit. I'm so excited to have you here. I've given a quick overview of who you are, but let's get straight into it. Most startups, most people are throwing words around like artificial intelligence, right? They have cooked that into their technology, into their product. But what is the difference between artificial intelligence and basic analytics that you could do in an Excel sheet? Rohit Pandharkar (02:25) Yes, that's probably the most thrown around world these days. If you hear any startup pitch, people will say, oh, we are powering this using AI. Oh, this is backed by AI, because of AI this will be better, because of AI this will be cheaper, faster, etc. But not everything in analytics is AI. The way I would like to think about this framework and I often talk about this wherever I speak, is four stages of data science or analytics. First is descriptive analytics, which is just describe what happened, let's say Mumbai rainfall. There was a cloud burst in Mumbai on a certain day. How many mm of rainfall happened? That's descriptive analytics. Second stage is diagnostic analytics, which is why did it happen? Is it some cloud burst? Is it some other climate change that could describe the diagnostic piece of it? Again, this is not really AI. It's again, an analytical way of looking at things. Third piece is predictive analytics, which means tomorrow what would happen, when will it happen, and what would happen? That piece is the real machine learning piece because you are using historical data or some learning to predict what will happen in the future. And fourth piece going beyond that is prescriptive analytics, which is if you are predicting something will happen, maybe something good or bad, how do you make it better? How do you make it happen earlier? Or how do you avoid that from happening? That is prescriptive analytics and predictive and prescriptive analytics piece is actually AI. Whenever you are using historical data or some learning data to predict certain outcomes or prescribe what to do better is actually AI and not just analyzing things in an Excel sheet, but actually using algorithms, sophisticated algorithms like Xg boost, deep learning, etc. is the real AI. Darshan Doshi (04:17) Brilliant. One of the things that Dasar we try to do is to simplify, make it really easy to understand, leave out the jargons and focus on action, performance, we talk about productivity, we talk about getting things done. So thanks a lot for decoding what AI is and what is the difference between analytics and AI. I was part of Nvidia a few years ago where they had put together a startup event in which they wanted to support startups working on machine learning and deep learning. And it was really amazing the kind of talent we have in India which is coming through right? The startups that are being funded to solve some really core critical problems. So my next question to you is what kind of data or how likely is it for a business to use machine learning, AI to actually create something tangible which can be used by people in day-to-day lives? Rohit Pandharkar (05:19) This answer is actually given by Andrew Ing and I would just repeat what he said is that probably the highest value addition by AI is going to happen through supervised machine learning. What is supervised machine learning? Supervision of historical data to learn patterns and predict a new item presented to you so that you can figure out what will happen, when will it happen and how to avoid it, or how to make it happen better. For example, think of a loan disbursement. If I could predict which loan is likely to go bad, that's immense value add for a financial services company. Think of a product sales company, say car sales or tractor sales, or selling electronic equipment like television or refrigerators. If I could predict the demand of TV sets for a certain brand around Diwali in India, that is immense value added because all my supply chain, logistics, procurement, marketing can be designed accordingly. Think of again, transportation. If I could predict what is going to be my cost of a trip, say an Uber trip or an Ola trip from place A to place B, that is adding tremendous value and that is happening through supervised machine learning, which means I'm looking at historical trips, historical TV sales during the Valley or Historical Loan NPS and learning from that, what will happen to the next case or next question in front of me? And as a CXO or as a leader of any company, one can add value by being prepared for it and making things better. Darshan Doshi (06:50) Brilliant. You remind me. So one of the startups that I've invested, the name is My Auto IQ. It's a US-based start-up by some awesome founders. What they do is they use machine learning to really help car dealers understand within the five mile radius what is the probability of a person looking to buy a car and help in improving the conversion rate for these car dealers. And it's such a strong use case. And these guys are using various data points because the data is formalized. It's a little bit more structured in the US. And so my next question to you is, all right, I've identified a use case. I think you've convinced on how people could our businesses could drive value out of supervised learning or any of the other technologies that you said. But one of the core questions is forming teams. Since COVID, the demand for data scientists has gone through the roof. And I don't know precisely what is the demand and supply within this talent pool that we have in India or around the globe, but it looks like everybody wants a 100% to 200% hike, right? Whether you're in Bangalore or Pune or wherever. So how does one business, if it is looking to build an in-house data science team, go about hiring such talent? What are the things that need to come together to hire an A-plus quality team? Rohit Pandharkar (08:32) Let's first talk about an ideal data science team structure or an ML team structure. It has three core pieces. One is the data scientists, folks who actually write algorithms, build the predictive models. Second is data engineers, folks who would manage the data lake, data warehouses, the privacy data governance, etc. for pieces. For example, in Europe you have GDPR where a lot of Privacy concerns like amnesia, which means forget everything about me on your platform. If I come and say I want to delete my account, you should forget everything that you have about me on your platform. That should be doable things like take out, which means I want to download all the data from your platform and go away should be also possible and things like storage. Some of the companies, like financial services companies in India are required to store all the data within the geography, even if it's on cloud. The servers have to be located within Indian boundaries and accessible. So to manage all that, you need data engineers who would write your queries, the ETL jobs, the pipelines for real time data extractions, event streaming platforms, etc. And third piece is business intelligence. Business intelligence is all about visualization dashboard, MIS Management Information Systems, something like a CEO cockpit, which will allow the CXOs or the CEO to look at how many products am I selling? At what margin did I sell them? What's my forecast for next three months? What am I likely to do in this quarter? How would the training twelve months look like, etc. So these are the three pieces of a data science team, which is data scientists, data engineers, and business intelligence analysts. To hire these people, actually, there is this concept of an imaginary unicorn data scientist, right? Nobody really gets that. There are very few people in the world today who would be like the best guys in building, let's say the driverless cars or the best algorithms in natural language processing or computer vision. And they probably run into millions of dollars of comp per year. And there are cases where people have said that driverless car data scientist engineer or an ML engineer would be way more costlier than some other leaders as well in the company. So rather than running after this unicorn data scientists, I would say one should look at people who have the right set of skills. For example, if you take any machine learning book or any machine learning course, it all has the same set of algorithms or problem statements, right? You have regression, clustering, segmentation, binary classification, say Xg boost models, random forest models, deep learning, hierarchical clustering, etc. If someone knows 6-7 of these standard techniques, which is something that is like a toolkit to solve most of the business problems, I would hire that person. At the same time, what is happening is because of COVID and remote jobs being available, there are plenty of jobs and too much demand for these kinds of data scientists. And to be able to be attractive in such a situation, companies can do many things. One of the things one could do is offer complete remote work. That is always a win win. Second thing one can do is and I've seen several companies that I know following this strategy, which is give your best number in the first goal, which means don't try to negotiate, don't try to see where the benchmarking is, where the current candidate compares, etc. Just give the best budget number you have and say take it or leave it because it's the best we can do and that's likely to be workout, something that will work out better. Other than that, what you also need to offer is seat at the table to the data scientist, which means in most of the companies I have worked in, CXO level discussions should happen in the presence of data scientists when a particular data science problem is being discussed. For example, if you are thinking of solving the NPA problems for a bank or a financial services company, the data scientists should be able to stand up and explain the story the data storytelling to the MD or CEO of the company. They should be part of the vision and strategy towards how to use data to do better business. This exposure to leadership and then finally rotation among the projects is also important. No data scientist wants to build the same model repeatedly every quarter to just refresh the model and manage ML Ops. He should get exposure to different types of problems, let's say marketing data science problem, operations data science problem, financial data science problem, or any decision making for the CEO, etc. So with these four things, which is remote work, give the great best comp possible, third is exposure to leadership and fourth is rotation. If you do these four things well, you can hire and retain a top data scientist. Darshan Doshi (13:29) So thanks a lot for breaking that down. I have two points to it, but I'll first follow that up with a question because many of our audience members are early in their careers. And one of the things that I keep focusing on Dasar is you might not have the expertise or the background or the education background, but if you are excited about something so you want to enter the data analytics space, you want to become great at machine learning, put your efforts, get your hands dirty, get in and get started. Certification or no certification, you'll find your way if you like it, right? So for someone who wants to get started in this field but has no education background in this, any suggestions of how a person could get started? Because we do want to build a big talent pool where India becomes a force to reckon with, right? So maybe some thoughts on that. Rohit Pandharkar (14:27) There is plenty of material available online today for someone willing to make a career change in data science. You may find several AI influencers AI YouTubers who would have YouTube channels where you could actually get a six-month roadmap with segregated modular videos to learn different things and I'll explain how that works. First thing you should do is probably take the AI for Everyone course by Andrew Ng on Coursera that talks about what is AI, why this is useful today, and what problems it can solve. Next thing one could do is start learning basics of Python. Python is the de facto language for machine learning and data science today. Third thing you could do is slowly start taking practical caggle challenges or any challenge online. Which means take a data set of say COVID patient chest X-rays and try and predict whether the person has COVID or not using your binary classification algorithm. Because if you can classify between yes and no probabilities for let's say a chest X-ray, you can also classify whether a car is damaged or not or whether a loan will go bad or not, or whether the crop is having a disease or not. Right? All these are very similar categorical problems of binary classification. If you do that, once you can prove to your prospective employer that you can solve such problems. Another thing you should also do is learn several tools like say, tableau. Tableau is great for business intelligence and visualization. There is a great dearth of tableau experts or Power bi experts for that matter, who would be able to build great dashboards for MIS, CXO level reporting and management cockpit. Another thing one could do is take up AWS certifications or Microsoft Azure certifications where these companies are offering even Google cloud certification in data science or data engineering, where for free, these companies are offering video self paced courses on their websites to learn how to do data science using AWS or Google or Azure tools. You could do this in six months and I'm confident that if you do this with enough sincerity and your own consistency, you will be able to make a career change into data science, data engineering, or business intelligence. Darshan Doshi (16:44) Yeah. And I think you have to give yourself a good three to five years runway as well. If you are really interested, I think six to eight months gives you enough time to understand. Am I passionate about this? Do I really like it? And after jumping into it, do I still continue to like it? Is this something that I am excited to do? We have another guest coming on this podcast. His name is Darshak Shah. Studied with me at Babson. He's worked in large companies as well as early fintech startups heading their data science and building data science teams. Now he's started his own services shop in data science. And we'll go deep dive on that podcast of how he's actually facing a challenge of where people are not interested enough to jump in and to get started when he is ready to give them a break. And so there are people, if you are interested, there are people who will help you along the way and we'll bring you those people. So let's get to the next part. Examples there is nothing better to showcase value of any technology than through a real example that you may have gone through and you've been in some fantastic positions where you had a chance to influence this technology and bring it to life. So maybe from your experience, without naming names, if you can share the context of the problem that you are trying to solve a business problem, how did you go about it and what was the outcome? If you could do that in just a short time, that would be great. Rohit Pandharkar (18:22) Before we get into a real corporate example, I also wanted to touch base on a very important thing, which is the change happening at all echelons of corporate levels, which means at the board of director level, companies are finding it crucial to have a data science representative. Companies, publicly listed companies are looking at data as an asset on the balance sheet, which is an amazing change because this was not the case a few years ago I have seen publicly listed companies appointing a director on the board who is an expert in artificial intelligence or data science so that the strategy for using and leveraging data comes top down. And there is discussion happening on how we are using AI at the top, most leadership level in these areas. So having said that, I want to give an example of something that I am very proud of from my previous career is having built a credit scoring and risk engine for rural credit dark or data dark customers. As we know, a lot of people in India do not have a credit history. Most of them have just recently started a janthan account and have taken loans but through informal channels. And since these loans are not formal like from a bank or an NVFC, there's no reporting on these loans to the credit bureaus and hence if you search for their civil score, you will either get a no hit, which means this person does not exist in the Bureau records or you will get a minus one score, which means there's not enough credit history to give a score to this person. Now how do you still lend to these people? There is an opportunity to lend to maybe 70-80 crore such people who are yet to get a very strong credit score. There is also another segment called subprime lending which is giving loans to a low credit score customer base. To do all this, you need to have augmented data or alternate data scoring. Which means can you look at say with customer consent and with the legal constraints available, can you look at the phone that the person owns and the model that he owns, the screen size of the phone, the data usage that person is making? Does he have some apps on his phone? For example, having a ride healing or cab healing app on your phone puts you in a certain strata of the society. Or let's say having a location history where you have checked into five-star hotels or some expensive malls can tell you a lot more about a person. Not necessarily that you may have gone and shopped into the malls, but frequent correlated visits to some other locations which are premium can tell you a lot more about a customer. Using all this alternate data through customer concept if we could build a credit scoring algorithm for people with low credit history or no credit history, it can change the fabric of credit access for rural India. And in one of the publicly listed large NBFCs that I've worked with previously, we have tried using different shades of alternate data. Not necessarily the examples that I gave earlier, but rural data points to be able to decide how to give affordable credit to these guys, which means at a lower interest rate to larger set of people whom the traditional banks consider as a risky profile customer having done that one could impact say tens of billions of dollars of AUM and grow the business and reduce the NPA. It does good for people as well while you're doing it this is a real example of AI helping someone get out of the sahukari, right? What's the option for the rural customers otherwise? If no bank gives him a loan to go to a sahukar who charges these pahadi interest rate loans, which means crazy amounts of interest, 10% per month, 6% per month, which amounts to compounded over twelve months, a huge interest, that leads to a dead trap for the rural customer. If instead some companies are able to lend to these guys at a reasonable rate of interest and still manage profitability, that can change the way these people improve their lives, get access to education, health care, working capital, start their businesses and so on. So I'm very proud of this particular example where I got a chance previously to impact this particular area using AI. Darshan Doshi (22:46) I'm so glad you touched upon this example because one of the focus areas of DASAR is financial independence. And one of the things that we say is once you are financially independent, you can do things that you only dreamt of. And really make an impact on the society. Some of the things that stood out for me in the example that you gave were just mind boggling numbers, 70 to 80 crore people in India who could get access to credit which otherwise they would not get. Now just think about it for a moment. We know India has 1.3 billion people, but most of the people are still finding it difficult to get out of the financial dilemma. To have basic things like house, electricity, water, working capital, education for their children, which many of us in the metros kind of take it for granted. So I think the power of this technology is huge. I think also from a GDP per capita basis, India is moving from 1800 to about 2600 over the next four to five years. So that should also bring a lot of our people out of getting access to credit is very important to get out of that wheel, so to say. So thanks a lot for that quick example. Now I want to go back to hiring people. Okay. In hiring people, one of the things that you mentioned is you could do this, you can build all of this team. I'm also a part of a leadership development SaaS company called Adeption, started out of New Zealand. I've built a tech team here in Pune and we have a sales team in the US. And so one of the things that we do over there is while we don't have an in house data science team, we have a partner in shape of a local partner who's been helping us do some machine learning. And so over there also some of the conversations we've had is hiring people is very difficult. Hiring interns is easy. So they have a battery of like 50 interns and then their filtration happens and only about five, six, maybe ten of them last into a full time job who have the skill set. So when you are hiring your own team or building your team, what are the parameters that you are looking at? Or some thoughts around hiring when you're trying to build a data science team? Rohit Pandharkar (25:29) So I look for three aspects. One is the ability to think mathematically and that comes from simple, say, back up the envelope questions or guestimate questions, right? I would start with a guestimate to just see how exhaustively someone can think about a problem. Say how many kids under the age of twelve are there in Pune today? Let's pick a Metro city and ask the person, can you think about this data problem? What aspects of data does he think of? Can he go to government databases? Can he go to school databases? Can he refer to some data points like how many people appeared for HSC examination or SSC examination? What can change the data? For example, people traveling in and out of Pune? What can add more authenticity to the data? For example, the number of people who are appearing for IIT JEE, etc. So that kind of guestimate question would tell you the mathematical thinking and data exhaustiveness of the person. Second aspect I would look at is actual programming and statistical abilities to know the algorithms. I would just ask, hey, if you want to solve a problem where an airline is suffering from overbooking problem, right? There is a 200 people seat airplane and several people cancel at the last minute and you have to sell more than 200 tickets just to make sure that you have no seats empty when the plane flies. How much of overbooking should you do? Now tell me, which algorithm would you use for this? What data would you use for this and how would you write this code in a pseudo code language. That will tell you a lot about the ability of the person to appropriately go back to his repository of algorithms, repository of data sets and be able to think in terms of business problem statements as to why this problem occurs, why this is important for the business, how if this is solved, it will make profitability for the company, etc. The third aspect I would look for is attitude. Attitude is all about different aspects like are you willing to go on the ground and inquire to learn about how the business of the company works? What is the real problem? For example, overbooking, right? Think of hotels and resorts. Can you actually travel to a resort and talk to 20 customers in the resort to figure out why overbooking happens or cancellations happen and why people are unhappy about a certain booking experience? If the person is willing to get his hands dirty, travel and also be able to tell the story back to his leadership team and translate the problem. That is also a piece of softer skills that a data scientist is required to have. So these are the three things I would look for. Darshan Doshi (28:23) Brilliant. Rohit, one of the three areas of DASAR is performance, peak performance or productivity as well. So we have personal finance, we have fitness, and we have productivity and performance. And you kind of exemplify a person who's really good at what he does. And our aspiration is that we want to help people be the best at what they do. Whatever it is that they choose to do, we want to make them the best at what they do. And so even in your hiring process, I'm hearing instances where you're talking about the softer skills, the hard skills, the thinking capability, the doing capability, getting your hands dirty, going out and talking. And that's what elevates the performance. My next question to you is how can this technology, artificial intelligence, machine learning, even basic analytics, how can it help a person improve his or her performance or productivity? Have you got any instances, some thoughts? Because tools are there for us to improve and to use. And the way I look at this is this is a technology which is used as a weapon to suit myself, to make myself like Iron Man, for example. So maybe some examples or thought process of how you see this technology improve human performance? Rohit Pandharkar (29:51) It's a great question because we are at a stage where a lot of things that humans do very skillful things are actually being done by AI. And one piece I see is actually using AI as a pacer, like in a marathon. You have a pacer with you, right. To use AI as a pacer who shows you what things could be done and in what way they could be done as the best standard. It could be thought of in a very different way as well. I remember a company in Silicon Valley that I had once met called Drushti.ai, where actually AI is being used at the manufacturing floor for the person at the assembly line, let's say a phone assembly line where say a smartphone is being assembled. If you pick up the gasket or say the back cover of the phone and put it on the chip and turn it around and tighten the screws, this task would be evaluated by an AI and could be timely given feedback on. The moment you do something wrong, the AI will give a buzzer and tell you that, hey, this particular task, third task, out of the three tasks that you are supposed to do is not done properly. That is at a very generic level, maybe at a factory level feedback. Right. At a very high level, let's say for someone who is a remote worker or a programmer, actually AI is doing things like Copilot, which means while you are programming, AI completes the code. And there are things like just describe what you want and it will create an image or an impression clipart like Auto Draw or some NLP driven and generative adversarial networks driven gain driven algorithms now that let you say, hey, I'm making this slide and I want a person working on the factory floor using a night vision camera looking at an auto assembly line. And it would paint that picture automatically. So you don't have to look at a different slide or go to Internet and search it. You will actually dictate what you want and it will generate that image. So that's personal productivity through AI. Another thing that we often can do nowadays is, by the way, use speech recognition for dictating tasks, dictating emails, dictating work, and actually Summarizers. We are working with a company as a project to do legal summarization. See, advocates have to go through so many legal court case documentations before they prepare something quite precedent for a case. So if you had to read 200 pages to come prepared for a particular case you are solving, it consumes a lot of time. But AI today can actually summarize all those 200 pages of documents and give you a three page summary saying, this is what the precedent in such cases is. So these kind of auto Summarizers, auto image makers, auto task trackers can help you pace up your work. Darshan Doshi (32:56) One of my favorite companies since my days in Boston at Babson was Boston Dynamics. It's a robotics plus AI company and they've created some of the best robotics out there. And you kind of reminded me of how one of those robots was able to not just open the door, but do a bunch of things and including dance. So the movements to mimic human dancing is also there. So that was just fascinating. That leads me to my next question, which is what is the future for humans with respect to AI, maybe even robotics, if you are willing to do this, I know you were at MIT Media Lab, and so I'm sure you got exposure to robotics as well. So for the audience, maybe a quick look into the future from your eyes, your opinion as to where are we heading with respect to these technologies? Rohit Pandharkar (34:01) I am a big fan of the concept of singularity and I believe that maybe around year 2045 we may achieve singularity. What is Singularity? The moment in time when an artificial general intelligence becomes as intelligent as one single human brain and can do all the tasks that a human being can do, like painting, making poems, singing, playing synthesizer, playing guitar, thinking critically about, say, a legal court case, a decision on a court case using precedence, or a business decision on where I should open my next branch or which product should I launch. There are AI CEOs of companies as we speak, which are taking decisions on behalf of the enterprise on what is to be done. Google has recently launched multiple language translations and also ability to call a Starbucks or any coffee shop or a salon where you can just talk to an AI and book an appointment. All this is happening in 2022. By 2045 you would reach a level where you are talking to an AI and won't even figure out at the other end which is passing the tuning test whether you are speaking to a human or an AI or a robot. But I am more worried about the way we will be able to digest or not digest this. Imagine if all this is achieved, you would all be superhuman, right? Want to learn calculus? Just install an app for calculus in your extended brain and within a second you would be an expert in calculus. Want to learn how to play guitar like Jimi Hendrix? Just install the Guitar app in your super extended brain and you would be playing guitar like the world's most expert guitar player. But if this is possible, would anything would be worth doing? Because if you could read any book in 2 seconds by downloading that book in your extended brain or ask for any question and get the most accurate answer to an AI, we would not be human, right? Because being super human means losing the human capabilities. And I think our very life and the spice of life is about the limitations and errors we make. The incompleteness that we have in our human life. For example, I enjoy someone who plays guitar better than me because I probably would have to spend years learning guitar that way. Or I admire Ussain Bolt because I can't run like him, but if I can do maths like a Fields medalist, if I can be as intelligent as the most intelligent designer in my own designs, there'll be no segregation left. So where would that excitement challenge of doing something better in life be? Hence, I think it's better we prepare ourselves for a hybrid future where the very humanness, the emotions, the incompleteness, the inadequacies of human life are still maintained. But we are able to live a happier life because of AI and because of robots. Darshan Doshi (37:12) Lovely. Thanks for sharing that. I think there are pros and cons of everything. And so you've kind of laid it out here is where we are today in this podcast to kind of just quickly sum up, you've simplified what is AI? What is machine learning? How does it differentiate from analytics? What are the use cases? Where is this technology today applied in the world? How can it uplift masses of people who may not have access to capital, who may not have access to credit, it can improve life and livelihoods. But at the same time, in the future, the pace at which we are going, we are going to face new challenges, new problems. Nothing better than the pandemic to kind of show this right? Over the last two years, 18 months when everybody was locked in in the lockdown you are extensively most people were extensively on an average people were spending 11 hours 33 minutes on their phone or on devices. And that basically means that's the amount of time you've not spent connecting with anyone else, a human talking to a human and that has shown higher levels of anxiety, depression and some of the other challenges. On the other hand, you've been able to still connect with people across geographies. FaceTime your family member in the US or in Australia while they were still in the lockdown and still maintain your connecting with your grandchildren which there is no filter there. You can see everything, you can talk it's real time and it's awesome. I hope you love this podcast. Rohit is one of the most articulate people I know. He has shared his experience, expertise, opinions in this podcast. If you liked this please hit the like button. Share a Comment If you have a question about data science, reach out to us lastly go on dasar.in you'll find a bunch of courses over there. You'll find a bunch of podcasts writings to help improve performance, personal finance and fitness. I'm Darshan and stay tuned. Thanks. If you liked this podcast, you may also want to listen to: * Decoding the Indian Fund Management Industry | Mandar Mhatre | Investing | Personal Finance | Wealth | Stock Market * Gaining Financial Independence | Darshan Doshi | Personal Finance | Financial Freedom | Optionality | Career | Wealth * Why You Should Manage Your Own Money * D2C Brands | Creating #1 Consumer Food Brand | Ravi Nigam | Tasty Bite | IPO | M&A * Building Enterprise Tech Products | Shridhar Shukla | SaaS | kPoint | Video Analytics | Tech Services vs Products | DASAR * Building Billion Dollar SaaS Companies | Monish Darda | ICERTIS | Contract Lifecycle Management | Culture | Bikes

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