Vishnu V. Ram on Mentorship, Leadership, and Learning

Great leaders aren’t just experts in their field—they’re mentors, problem-solvers, and collaborators who empower others to succeed. Vishnu V. Ram, Vice President of Engineering at Credit Karma, has built his career on this foundation, balancing technical excellence with a deep commitment to mentorship and continuous learning.

Vishnu joins us on this episode of Keep Moving Forward to unpack the leadership lessons he’s learned across decades in tech—from navigating the dot-com boom to building resilient data science systems today. He shares how fostering a culture of trust, humility, and collaboration leads to stronger teams and better innovation, why mentorship should flow in all directions, and how embracing failure helps build more robust, data-driven systems.

2025-02-25  • 34 min

Vishnu V. Ram on Mentoring for Leadership Success

Keep Moving Forward

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Mentorship Flows in Every Direction

For Vishnu, mentorship isn’t confined to hierarchical relationships. Instead, it’s about finding someone who listens, challenges, and helps you grow — whether they’re above, below, or alongside you in an organization. “Mentorship flows in all kinds of directions,” Vishnu explains. “A lot of mentorship is about making sure that you’re finding someone who wants to listen to you, and who has the time to listen to you and also is able to help you… understand your problem better and help you understand the potential nature of the solutions better. It’s not about getting the answers immediately.”

Vishnu emphasizes the importance of creating an environment where mentorship happens naturally. This includes fostering relationships where both mentors and mentees feel comfortable sharing insights and challenges. By valuing mentorship as a two-way street, Vishnu ensures that his teams benefit from diverse perspectives and continuous learning.

Leading with Humility and Collaboration

Vishnu’s leadership journey is rooted in humility and collaboration. Over the years, he’s learned the importance of not only solving problems alongside his team but also empowering them to find solutions independently. “You are helping them craft,” he says, “and they’re going to find very different solutions than you would have found working closely with them… You’re helping them grow for the future in a way that you would have never done if you just sit with them and solve their problems together with them.”

This approach extends to his interactions with stakeholders. Vishnu highlights the value of stepping back to understand others’ needs, especially when building tools or systems. By fostering a culture of collaboration and inclusivity, he ensures that his teams and stakeholders are aligned on goals and solutions.

Building Resilient Data Science Systems

In the world of data science, understanding failure modes is as critical as achieving success. Vishnu stresses the importance of recognizing the probabilistic nature of data-driven systems and preparing for the inevitable challenges that arise. “If you’re talking to other stakeholders who are not data scientists, they have a lot to share about how things can fail, and that will allow you to succeed better,” Vishnu says. “And that will allow you to also protect your users’ interests and your business interests better.”

By engaging with stakeholders and anticipating potential pitfalls, Vishnu’s teams build systems that are not only innovative but also resilient. His focus on structured data and tools like TensorFlow has enabled Credit Karma to drive meaningful business outcomes while maintaining flexibility and reliability.

Transcript

Vishnu V. Ram:
I strongly believe that mentorship is not related to who you report to or who reports into you. I think mentorship flows in all kinds of directions, and it's really hard to force mentorship to happen. What I will say is that a lot of mentorship is about making sure that you're finding someone who wants to listen to you, and then who has the time to listen to you and also is able to help you with some anecdotes from their career and their work life.

Caleb Brown:
Hey everyone, and welcome to Keep Moving Forward, the podcast from X-Team for tech professionals who are passionate about growth, leadership, and innovation.

I'm your host, Caleb Brown, and in each episode, we'll dive into candid conversations with the tech industry's brightest minds—seasoned leaders, forward-thinking engineers, and visionary experts.

I'm thrilled to welcome Vishnu V. Ram, Vice President of Engineering at Credit Karma, to the show. Vishnu’s journey is both inspiring and insightful, spanning early experiences in startups during the dot-com era to leading groundbreaking machine learning innovations at one of the most transformative companies in personal finance.

What’s remarkable about Vishnu’s story is how he blends technical acumen with a focus on mentorship and collaboration. In this episode, he shares how his leadership style has evolved through diverse roles, the lessons he’s learned from handling failure, and the importance of fostering continuous learning in teams.

Vishnu also provides a behind-the-scenes look at innovations at Credit Karma, including adopting TensorFlow for deep learning and building structured data sets that have driven meaningful business outcomes. 

Whether you're interested in data science, leadership evolution, or creating a culture of collaboration, this conversation offers valuable takeaways. Ready to dive in?

Caleb Brown:
Vishnu, thank you. Thank you so much for joining.
 
Vishnu V. Ram:
Absolutely. Thanks a lot. Caleb, for having me here.
 
Caleb Brown:
I do like to start things off with, just like I said, you do have a quite an impressive background, so I thought we could just kind of jump in with having you sort of walk through your career journey that's starting from your early days as a software developer to your current role at Credit Karma got
 
Vishnu V. Ram:
I started as an engineer at a startup sometime during the.com boom time. I was not working on any.com company, but I was working in a very small startup, which meant that I was forced to learn a lot of things very quickly, and going through the hype of the.com and then the crash that came along with it meant that there was a lot of compressed learning that I feel like a lot of people don't get the privilege of getting. And then after that, this is in the US. I was in Nashville, Tennessee during the time frame, and then I went back to India and started working at what started working at SIFI, which is an Internet service provider in India. I was building a network management system, and my CTO at SIFI gave me the responsibility to build out a network management system and build a team around building a network management system. 

So I did that for a few years, and that's when I got into the habit of focusing on learning, focusing on taking my team along while I'm learning, and also learning from them. And one of the things that my CTO there told me, that still sticks to me, is that if you're able to sit next to the customer and see how they do things, you're going to learn a lot. So when we were building the network management product, we were able to sit right in the network operation center and look at some of the systems and networks network people use the product, and that probably gave me more insight and how to build a great product, and how to listen to your customers, and the importance of listening to your customers, very early on in my career, after that, spent more than 10 years doing early stage startups in India. So this is like more CTO roles at early stage startups. A couple of those startups are, one is a listed unicorn, and the other is an unlisted unicorn, still operating in private mode. 

Then I decided to come to the US. That's why I had an opportunity to join Credit Karma. I joined Credit Karma late in 2014 I've been with Karma almost 10 years now, and while I had a lot of opportunity, working with small teams, working on a lot of green field projects, when I was at SIFI and when I was at my startups, I think at Credit Karma, I had an opportunity to work at a company which was already scaling, but there was still a lot to do. And where you have opportunity. When I was at the startups and when I was at SIFI, I didn't really have a lot of peers that I was working with closely at Credit Karma. I had a lot of opportunity to learn from my peers who have been in the Valley for a long time, who had to face a lot of other situations that I had not had the privilege to learn from or to deal with. And so I would say it's been an awesome time learning everything that I had learned here at critical karma so far and continuing to learn here.
 
Caleb Brown:
Excellent. Very cool. Yeah. Well, you mentioned a lot of really interesting things. For one, I'm jealous that you got to experience the.com era. Obviously, the bust part, not so much, but it's such an exciting, interesting time for products and consumer products. But you also mentioned something that I think is really important, that I think even in 2024 some folks miss, which is that, you know, it's so important to talk to your customers, to see how your customers use your product. And so I just, I wanted to highlight that takeaway, because I think it's a really important one. So you touched on, I think in the last question, you touched on that you have been at Credit Karma for nearly a decade now, just always curious to kind of see what has kept you engaged and sort of excited, you know about that work for so long?
 
Vishnu V. Ram:
Yeah, I think during the initial parts of your career, you're just kind of going with the flow. You're probably have a few opportunities, and you take those opportunities and see where those opportunities take you, and then you are. It's easy to look back and think that you made great decisions, but then we all need to recognize that a lot of times, it's not so much your decision making capability that landed you in these great situations, it's more, I would say, just serendipity and luck. I think that is that's definitely a recognition that I had very early on, that you're going to make decisions, and those decisions are going to sometimes land you in good situations, sometimes land you in bad situations, but more importantly, it can land you in bad situations that. You feel are good in the short term and good situations that are actually bad. So I think that recognition and like knowing and having the humility to just talk to others around you who are not in the journey with you, and who can help you make some of those decisions. 

I think for I've always wanted to have a long stint at an organization. I remember the one of the co-founders at the gaming startup. I left them after three and a half four years. I remember him two years or something after I left the company. He gave a presentation to an educational institution. I think he was one thing that he mentioned as a great regret out there was that I did not stay longer with them. Yeah, and then I realized that it's great to hear that as an individual that your contributions were valuable for a particular organization's success and growth, but it's much better to hear that while you continue to be in the organization and continue to drive a lot of success there, yep, so I and I think there is also, you also get a lot of learning when you go through situations where you feel you're stuck, and you are able to just persist through the period of being stuck, and then you figure out a way out of it, and and that that's been, that all of that has been there for me here at Credit Karma, right? 

And I feel like the all of this, it comes from your leadership team and the people around you and your team, what kind of support you get from them that allows you to work through some of these issues with some amount of patience and persistence and humility, right? I think it's always, we always feel that we deserve more. We always feel that we can we are like the best in the world at certain things that we do. But having a little bit of humility allows you to also learn how you can get better and I think a lot of that comes when you're able to stick to the tough situations. 
 
Caleb Brown:
Absolutely, very well said, we've already touched on, you know, the fact that you've worked in a ton of different kind of organizations and different, you know, startups and things like that. So I am curious sort of, how your leadership style has evolved as you've kind of moved through those, you know, startup environments and then kind of larger organizations like, like Credit Karma, yeah.
 
Vishnu V. Ram:
I mean, it's very interesting some of the growth aspects there, right? And and again, I'll go back to saying that you learn a lot along the way in terms of how you should grow and how you could have grown, and how you could have done a better job. I think probably the first few stints that I've had, I've just had the privilege of having really good education, so which means that your the pedigree of your education means that automatically, the people around you have respect for you and respect for the potential that you bring along with that, and that can take you some distance, and then you have to put in the hard work, and you have to convert that potential into some kind of impact for the team, impact for the project that you're working on, impact for the business. And so what that means is that I had the luck to have had the opportunity to study in really good institutions, and then that, along with that, came a little bit of initial respect. 

And then along with that, also came confidence, right? So I didn't have enough knowledge and leading teams, I didn't have enough knowledge in helping team members go through go through problems and deal with challenging situations, but I had their support and I had their respect and I had their trust, and once I knew that I had their respect and I had their trust, that just gives me more confidence, and I've always been someone who's enjoyed working very closely with my team members, and which meant that we were they were willing to share, in all honesty, what they were facing and how they were facing those solutions, and then I was able to, just like, work with them in the trenches through some of these problems, right? 

So I think those were early learnings. Only later on, I also understood the power of coaching where, of course, you there is a lot you can do by just like working very closely and solving problems together. But when you get into coaching, you get into a coaching mindset. You're also setting up your team members to solve, find ways to solve their own problems by themselves, so in which case you are trying to, like work with them to ask the right questions so that they are able to find the answers, and they are able to find the answers, which maybe you don't believe in, but it's still their answers for their problems that. You are helping them craft, and you're helping them provide the support so that they can go along with those answers, and they're going to find very different solutions than you would have found working closely with them. And that means you are helping them grow, and you're helping them grow for the future in a way that you would have never done if you just like, sit with them and solve their problems together with them. 

So I would say that was a very big unlock. And then the next unlock is now that I have learned to be a good coach. Can I help my directs, and can I help other directors and others senior directors also learn how to coach well and figure out, like, what are the right situations where you have time to coach through a situation, or when, where or when do you decide, like, hey, you need to go and solve, solve together, or solve on your own, and then help that other person learn, because they just don't have enough time to pick up some hard skill.
 
Caleb Brown:
Yeah, yeah. I'm just curious the strategies, because you've been in the game now for a while. I'm curious the strategies you found for kind of effective communication of those complex data science concepts for some non technical stakeholders. Yeah,
 
Vishnu V. Ram:
I think that's definitely a very hard problem, and one of the things that you probably want to do is that you do not want to take some complex data science concept and go into a large gathering and try to explain an algorithm, right? So that's definitely a big no in general. Like if you're looking at your stakeholders, want to understand, how are things set up for success and when things fail, how are you set up to handle it? And how often do things fail? And how do they fail? They want a better understanding of that. The sometimes you will work with stakeholders who do not have an understanding that this whole process is more driven by data. There is some probabilistic nature to it and not it's not all deterministic, and you can't eliminate the probabilistic part of it. So I think the first step is to, like, help them understand that a lot of this process is, like, driven by data. And if you feed bad data, or even if you have the best data possible, you might still be missing something else which doesn't allow you to capture reality. And you have to be able to help them understand that it is probabilistic. 

But there are ways in which you can manage the uncertainty. You have to you don't have to go into telling them how you're doing it, but you have to tell them that, to give them enough information so that they are empowered to understand that and help you understand like, what is this use case about? Can it deal with like a 5% uncertainty, or can it not deal with it? If it cannot deal with it, then, if you need a highly deterministic system, you probably are going to find it more challenging. I think you have to work with your stakeholders to understand the nature of the use case, because more often than not, you do not have that knowledge, and you might think that you are still delivering a highly more valuable system, but your stakeholders might not look at it that way. Your partners, external partners, might not look at it that way. Definitely, the regulators might not look at it that way. So sometimes you you also have to know when you want to step back and say, Hey, we're not going to apply data science here for these reasons.
 
Caleb Brown:
That's very helpful. And you know, earlier we kind of touched a little bit on on mentorship, I believe, and so I was just kind of curious to expand on that, to drill into that a little bit, what role mentorship plays in your overall leadership approach, and kind of how you actually implement that within your teams?
 
Vishnu V. Ram:
Yeah, I strongly believe that mentorship is not related to who you report to or who reports into you, right? I think mentorship flows in all kinds of directions, and it's really hard to force mentorship to happen. What I will say is that a lot of mentorship is about making sure that you're finding someone who wants to listen to you, and then who has the time to listen to you and and also is able to help you with some anecdotes from their career and their work life, or from whatever books they have read or podcast that they have listened to to help you understand your problem better and help you understand the potential nature of the solutions better. It's not about getting the answers immediately. I think it's more about having an opportunity to just like discuss it out aloud, and having someone who's willing to listen but also willing to challenge you, and can you then, if they challenge, be willing to listen to their challenges and then give honor to the challenge. Do you respect the person enough to listen to their challenges? I think that those are the questions you need to ask before you get into like a mentor-mentee kind of a relationship. 

And I've definitely found people who I've managed, who have been my mentors in certain areas, and I have a lot of respect for them in terms of how they do their jobs, like I mentioned, like my first half my career was in startups, so which meant that I operate in a certain way, which meant that I didn't learn the ropes of growing as an engineering manager within an organization like ours, we have, we have, like 1800 people now and like, I didn't grow as a I didn't figure out how to get to become a manager in our in an organization like ours, which meant that someone who's learned how to do that and learned how to deal with all the challenges of being manager in a complex organization like ours has more skills than me. 

So then I need to first understand that they have they are a lot better at certain areas than I am so and then if they are challenging me in a certain area, then am I willing to listen to them? Am I putting myself in a place where I know that okay, if they challenge me, then it's an opportunity for me to learn from them, rather than looking at it as, Hey, I am the VP or not the VP. Why should I listen to you? Right? So I think it's finding the right people and also understanding that, like lot of this can there are things that will you you want to find have long term mentors that you can always go to, and you can always that's like, I don't want to, it's too crude a term. But just to land the point, there is comfort food, and then there is, there is good food for you to eat. So I think your long term mentors are maybe your comfort food, and then you are going back to them again and again. And then there are the other ways in which you there are so many other ways in which you can, like, get into this kind of a mentorship mode. Whether you are in the pro you are in position to mentor someone who you, who might be managing you, or might be multiple levels above you, or sometimes they are in a position to be able to do that for you.
 
Caleb Brown:
Yeah, that makes a lot of sense. So as you can see, you know, we do kind of focus a lot on, you know, the soft skills, of course, in this sort of, you know, the hiring and working together collaboration aspects. But you know, we do touch on the engineering side of it. And want to know if you could share an example of a kind of a significant improvement or even innovation that you and your team have made on the Credit Karma is machine learning infrastructure?
 
Vishnu V. Ram:
Yeah, I would say there are a lot of improvements and innovations that we just did that as part of your machine learning infrastructure. Lot of them are, I would say, very obvious. I would say, one of the early bits that we took 2017 time frame, 2018 time frame was just going with TensorFlow. So TensorFlow was open sourced by Google at that point of time, and TensorFlow was in its early days where they were doing dev summits, and they were like point three or something, but Google had published this paper on TensorFlow, and they had a lot more that that was happening behind the scenes, and they only open source some parts of it. And we took that and then we said, like, this is the way that we're going to introduce deep learning into into credit card. And then the way we went about it was we were able to build a team which build a quick POC, and it included both machine learning engineers and data scientists working hand in hand to understand how we wanted to build that I would say that is a big change for us, moving from traditional machine learning to deep learning models. But we all understood that we had a lot of data, and we wanted to be able to use all of the data to be able to drive our use cases forward. 

And the investment that we made at that point of time of using TensorFlow and building deep learning models on top of TensorFlow and other relevant Google GCP products like BigQuery and data flow that that that is a big deal for us when we did that, and it continues to pay a lot of dividends for us. And I think the we were able to find, we continue to find wins on top of that initial investment that we made. But the second important thing that we did was to be able to take all our data in our data lake and construct structured data sets, which allowed our data science team to be able to easily explore and easily use these structured, relatively clean data sets, to go and build their models on top of it, train their models on top of it. That, I would say was that, combined with our initial investment in TensorFlow, that is what has been paying a lot of dividends for us.
 
Caleb Brown:
Interesting, cool, and like I said, we do focus. It's in the title, we do focus a decent amount on the soft skills side and team management things like that. So just curious as we move along here, what soft skills do you think are probably the most crucial for folks like data scientists and engineers to develop in today's. Say, you know, tech landscape, because, as we've discussed, that landscape is kind of constantly evolving.
 
Vishnu V. Ram:
Yeah, I would say one of the important things that I've had to learn by making mistakes that you probably want to do is like, how good a job do you do of taking others around you along for the journey, and you're not going to be able to take them along for every part of your journey, but are you willing to step back and understand like, Hey, who are you leaving behind? And can you help them catch up with you? Is a very important soft skill, because there's a good chance that you are actually going in the wrong direction, and the only way you're going to find that out is if you're willing to step back and wait and talk to the person who you're leaving behind, and that might even change the direction, or in a very positive way, in terms of how we are traveling. 

This is more important than we also want to be inclusive of people who are going to get impacted by the work that you're doing. More often than not, you're going to learn the most. So if you're building something that, say, a marketing team wants to use, and if you're not talking to someone on the marketing team who's going to use what you're building, they might not use it at all. And then all the work that you put down is like they're never going to be put to use, right? It might be the best thing ever, but they're not yet using it. And then when they don't use it, then you're not getting value from it, because you've just built it in a way where they don't understand how to use it. It's not so much because they don't like you. It's just like they don't know how to use it. And you're not built it in a way where they know how to use it. 

So it's very easy for us to think that I like it works on my laptop, or it works on this data set. But you also need to know that the whole thing works, because you have other teams who are part of the solution here, and they're using the technologies that you're developing, they're using the models that you're developing. How can they use it? How can they play a role in being a part of that to really solve our member issues, right and business issues. So they are an important part of the solution that you need to recognize, and you need to be able to how to know how to take them along for the journey you that doesn't mean that they need to understand all the linear algebra and GPUs and whatever else that you're doing to have success, but you need to be able to help them understand the impact. You need to be able to help them understand how they're going to use the solution.
 
Caleb Brown:
Yeah, so and so. Obviously, every company, every organization, has kind of a slightly different culture and a way of doing things interested how you foster kind of a culture of continuous learning, which is probably important for this, this world, you know, professional development within your teams, just constantly being aware of what's new. Is there anything you do to kind of influence that culture within your team?
 
Vishnu V. Ram:
Yeah, I think one of the things that I feel like I've done well over the last few years, especially after I learned how to do it well, is to explicitly name your partner teams, and explicitly name the people on your partner teams which I'm going to work with closely, that are one of my directs is going to work with closely. And when you go through that process of explicitly naming the people that you're going to partner with, that you're going to work with very closely, then you have a list in front of you, then that list is going to then change over a period of time, now, based on how the project is going, or based on how your role or how the organization is shaping up. So then when you go back and look at that list, you know that, oh, I actually missed someone on this list. Then that's an opportunity for you to add them back in, right and or add them in for the first time around. 

So I think that helps you stay grounded on who you're working with as a team to build things together, and who you're learning from, and who you are going to allow to influence how you're doing things so that, I think that explicit naming of teams and individuals that you're going to partner with, rather than just like, go with the flow, is a very powerful mechanism to use and also making, let's say you're working with a lot of different teams, also recognizing that you're not going to be able to partner with all of them, right? So then sometimes you have to then go and say that, like, Hey, you're going to partner very closely with three out of the 10 people that you put on the list. If you have 10 people on the list, then you're not going to be able to partner with them. Well, you can have, like, loose connections, but you cannot have, like, really, the tight connection there that is also recognition, right? Then you're going to be able to say that like, Hey, these are people that I'm not working with very closely, who also, somehow my organization needs to work with very closely. Then you're also calling out very clearly, who is not getting your love and affection and care right. So then you figure out, like, how to find other ways to mitigate that part.
 
Caleb Brown:
Totally and so another one that I feel. When I speak with folks is there's just a huge spectrum in how people handle it. So I'm interested in how you approach giving and even receiving feedback in your role as a leader.
 
Vishnu V. Ram:
Yeah, I think you have to try to do it a lot, all the time, right? And that's the important part here, because if you do it all the time, then people know, and like, you have to ask people, right? And you have to, like, not worry about if you get into the habit of asking people, it also, you're also telling yourself that now is the time to listen if you don't ask people and then they give it on their own because you've done something, or they've done something, or they don't like something that you have done, then you are you might not be in a good position to receive it. So if you start by asking, and if you ask often, everyone knows that you're looking for feedback if and you are also telling yourself that you ask for feedback because you want to become better. So I think asking often is a very easy habit to cultivate. You don't have mean like you have 30 minutes with someone you don't have a lot of topics. 

Ask for feedback. You go through a hard project, you miss a delivery, you screw up something, ask for feedback. Take any and every opportunity that comes along your way to just ask for feedback. Then you're telling yourself that like, Hey, this is someone who's you are asking for feedback. You're taking the action, and then they're giving you feedback. And sometimes they might not, most times they're not going to give you feedback that you like, but you're asking for feedback, not so much so that you like the feedback that you're getting, it's to act on it and learn from it and figure out, like, what to do with it.
 
Caleb Brown:
Yeah, one question that I really wanted to ask you in particular, because other guests have not necessarily been in the position. I was very interested in how Credit Karma is acquisition by Intuit kind of impacted your role, or really just the overall engineering culture?
 
Vishnu V. Ram:
Yeah, I think we've had this is like now year four of our acquisition and what Intuit and Credit Karma leadership very clearly understood from very early on was that, like Credit Karma was a mature engineering organization with a lot of investments and technology and engineering, and our organization, that's working really well, and it continues to drive impact for our users, for our mission, for Intuit as well. 

So once that recognition was there, very clearly it was about identifying the most strategic areas in which Intuit and critical are specific teams in Intuit and specific teams in critical are going to partner together to achieve. That has been a game changer for us, to be able to make sure that the acquisition goes really where we were very clear, very early on, Intuit leadership and critical leadership catching up, figuring out, like, what are the top three things, or top five things that we want to achieve during every financial year, and then identifying the right teams and right leaders from Intuit and Credit Karma to work very well together on. And then the other part of it is also like, where as you as leaders on both sides continue to work together and achieving some early wins that allows you to go take much bigger swings, go after, like, much bigger strategic initiatives, which can deliver a lot more for the overall combined organization. 

And I think for the last year or so, like, I've been involved working very closely with Intuit to build on top of Genos, which is the Gen AI initiative that, like all of Intuit is adopting. And I lead the Gen AI initiative for critical I work very closely with my peers on Intuit, and I've had like, two, three year long relationship with some of them, which allows me to make sure that I'm building things the right way, so that when I build things the right way, then all of Intuit benefits, and not just critical are benefiting from that.
 
Caleb Brown:
Very cool, very cool. Just interested, if you could give sort of One Piece advice to aspiring, you know, data science leaders, what would that be, after your years of being in the field,
 
Vishnu V. Ram:
I think data science later leaders, I would definitely say you want to be able to understand the failure situations really well, and you want to be able to understand how to protect your team and protect your business from some of these failure situations. The reason I say that is that when you do data science, when you take all the data, you feel that you and then you tune your model for the data that you have. The data is not complete, like I mentioned earlier, as well. So because the data is not complete, you are the missing data, or the fact that you have some problems with the data that you're using to build your model might actually like block you from achieve. 

Being the success you want to have if you understand some of the failure situations better, and you'll understand the failure situations better. If you're talking to other stakeholders who are not data scientists, they have a lot to share about how things can fail, and that will allow you to succeed better. And that will allow you to also, like, protect, protect your interest, your users interest and your business interests better.
 
Caleb Brown:
Good stuff. Well, thank you so much. Vishnu, I really appreciate this is a great chat. I feel like I learned a lot. To be honest with you, this is really nice.
 
Vishnu V. Ram:
Thanks a lot. Thanks a lot for the opportunity.

Caleb Brown:
What an inspiring conversation with Vishnu about blending innovation with leadership that puts people first.

I was particularly struck by how Vishnu’s approach to leadership has evolved over time, especially his emphasis on mentorship and creating a feedback-rich culture. He reminded us that great leadership isn’t just about the technical solutions – it’s about empowering people to grow, learn, and bring their best selves to the table.

His insights into fostering continuous learning and his candid reflections on handling failure were both practical and profound. Vishnu’s stories highlighted how collaboration and humility can unlock not just technical success, but true team synergy.

Thank you, Vishnu, for sharing your journey from startups to Credit Karma, and for the thoughtful lessons on building teams and technologies that make an impact. And thank you to our listeners for being part of this conversation. It’s stories like these that remind us why we keep moving forward.
Join us next time for more insightful conversations with tech leaders who inspire us to grow, lead, and innovate. Find us on Apple Podcasts, Spotify, or YouTube Music, and don’t forget to share this episode if it resonated with you. Until next time!