Padmajeet Mhaske is a highly accomplished leader in the field of artificial intelligence and machine learning, with over 15 years of experience driving innovation through large-scale AI/ML Experiment platforms. As Vice President and AI/ML Experiment Platform Architect at JPMorgan Chase, Padmajeet leads the Data Technology Team, where he is responsible for developing and scaling AI-driven solutions that have a direct impact on business outcomes. His journey in the world of technology began with pivotal roles at leading organizations such as UBS, Change Healthcare, Bank of America, and Accenture. During these years, Padmajeet worked across Data Technology and Portals, building the foundation for his current work in AI/ML.
At these organizations, he gained valuable experience working in the Research IT space, where data experimentation and insight development are critical to driving financial decision-making. His work in Data Technology—designing platforms and systems to support large-scale data processing and insights—has been instrumental in shaping his approach to building scalable and secure AI platforms in the banking industry. This unique blend of experience in both technical innovation and business strategy enables Padmajeet to seamlessly align AI/ML initiatives with the rigorous standards of the financial sector.
With his deep understanding of both the technical intricacies of AI and the strategic needs of the business, Padmajeet continues to play a key role in driving the future of AI in finance. In this interview, he shares his journey, leadership philosophy, and vision for how AI is transforming the banking industry.
David: Good afternoon, Padmajeet. Thank you for taking the time to join us today. To start, could you share a bit about your journey and what led you to your current role as Vice President and AI/ML Experiment Platform Architect at JPMorgan Chase?
Padmajeet Mhaske: Thank you, David. It’s a pleasure to be here. My journey into AI and machine learning has been both a personal and professional evolution. It all began with my academic foundation in data science, where I first encountered the transformative potential of AI. After completing my Master’s degree, I had the privilege of working with some incredible organizations like UBS, Change Healthcare, and Bank of America. These experiences were pivotal in shaping my understanding of how AI and machine learning can be applied to solve complex, real-world challenges, particularly in large-scale data environments.
Throughout my career, I’ve been passionate about creating innovative, scalable data platforms that can unlock business value, and that passion eventually brought me to JPMorgan Chase. Here, I’m fortunate to lead the AI/ML Experiment Platform within our Data Technology team. My role involves overseeing the development and deployment of high-performance AI/ML solutions that not only enhance our technological capabilities but also drive strategic business outcomes. In the financial services industry, where data is a critical asset, I focus on building platforms that can adapt to changing needs, deliver meaningful insights, and provide a competitive edge in a rapidly evolving market.
The intersection of cutting-edge technology and its ability to drive business growth has always fascinated me, and I’m excited to continue pushing the boundaries of what we can achieve with AI at JPMorgan Chase.
David: That’s impressive. AI/ML has become a cornerstone of many industries, especially finance. How do you see the role of AI evolving in the financial sector, and what impact do you think it will have?
Padmajeet Mhaske: The role of AI in the financial sector is not just evolving—it’s transforming the industry in profound ways. AI has already become integral to many areas, such as fraud detection, risk management, personalized banking, and customer service, and its applications are expanding rapidly. One of the most exciting aspects of AI is its ability to process and analyze vast amounts of data in real-time, which enables financial institutions to make smarter, faster, and more informed decisions.
Take fraud detection, for example. AI-driven models can identify patterns in transactional data that might indicate fraudulent activity, often in real-time, which significantly improves security and minimizes losses. This is a far cry from traditional methods, where detecting fraud could take much longer and involve more manual processes. But AI’s impact goes beyond just improving security—it’s also enhancing the overall customer experience. Personalized banking, powered by AI, allows banks to better understand their customers’ needs and preferences, offering tailored solutions that drive customer satisfaction and loyalty.
As AI continues to mature, I believe we’ll see even greater integration of machine learning models across all facets of finance, from predictive analytics for better investment strategies to AI-powered risk models that provide more accurate assessments of market conditions. Of course, as we push the boundaries of AI in finance, it’s essential to ensure that these technologies operate within robust regulatory frameworks, balancing innovation with compliance and ethical considerations.
Looking ahead, I see AI becoming a cornerstone of not only how banks operate internally but also how they engage with their customers, making services more secure, personalized, and efficient. Ultimately, the transformative potential of AI lies in its ability to unlock new value—creating more agile, responsive, and customer-centric financial services that can adapt to an ever-changing market landscape.
David: It’s clear that compliance and regulation are critical in the banking industry. How do you ensure that your AI/ML Experiment platforms adhere to these requirements?
Padmajeet Mhaske: You’re absolutely right—compliance and regulation are fundamental in the financial sector, especially when working with AI and machine learning. At JPMorgan Chase, we prioritize these aspects by embedding compliance and regulatory considerations into every stage of our AI/ML platform development process.
First and foremost, we ensure that all of our AI models are built with transparency and interpretability in mind. This is crucial not only for compliance purposes but also to ensure that stakeholders—including regulators—can clearly understand how decisions are being made. We employ a variety of techniques, such as explainable AI (XAI), to ensure that our models are not only accurate but also accountable. This transparency builds trust and ensures we meet regulatory requirements for fairness and explainability.
In addition, we maintain a strong focus on data governance. Data privacy and security are paramount, and we work closely with our legal and compliance teams to ensure that all data used in our AI models complies with the relevant privacy laws, including GDPR, CCPA, and other global standards. We also continuously monitor and audit the data pipelines to ensure that no sensitive or personally identifiable information is misused.
Another important aspect of our approach is agility. Regulations are constantly evolving, and we must stay ahead of these changes. To ensure that we are always in compliance, we have a dedicated team that monitors regulatory changes and collaborates with both legal and compliance departments to implement any necessary adjustments to our platforms.
Ultimately, maintaining compliance is not just about meeting regulatory requirements; it’s about fostering a culture of ethical AI and ensuring that our AI/ML platforms are designed to serve customers fairly and securely. We believe this is essential not only for compliance but also for building long-term trust with our clients and stakeholders.
David: Collaboration seems to be a key theme in your work. Could you tell us more about how you work with data scientists and engineers on your team?
Padmajeet Mhaske: Absolutely. Collaboration is at the heart of everything we do, especially in a role like mine, where cross-functional teamwork is key to success. My primary responsibility is to translate the needs and goals of data scientists into scalable, high-performance platform solutions. This requires continuous communication to ensure that the infrastructure we build can support the complex requirements of their models.
I view my role as a bridge between the technical and business teams, which involves a lot of back-and-forth. We work closely with data scientists to understand their model architectures, datasets, and computational needs. In turn, we ensure that the platform is optimized for these models and can scale effectively as they grow. This kind of collaboration ensures that we are not only meeting technical specifications but also enabling the team to experiment and innovate more freely.
A critical part of our approach is fostering an open environment where ideas can flow freely. Whether it’s through brainstorming sessions, regular team standups, or collaborative workshops, we make sure that everyone has the opportunity to contribute. Innovation often comes from diverse perspectives, and it’s important that everyone feels heard and supported in sharing their insights.
We also follow Agile methodologies, which allow us to work iteratively and adapt quickly. This approach helps us incorporate feedback rapidly and respond to new insights, market changes, or evolving business needs. Whether we’re refining a platform feature, optimizing a model’s performance, or solving a technical challenge, the ability to adapt quickly is essential.
By maintaining close collaboration with both data scientists and engineers, we ensure that the solutions we build are not only technically robust but also closely aligned with the business goals. Ultimately, our collective focus is on creating platforms that are scalable, efficient, and capable of supporting the AI/ML experiments that drive innovation across the company.
David: With such a fast-paced environment, how do you stay on top of the latest trends and advancements in AI/ML?
Padmajeet Mhaske: Staying on top of the latest trends in AI/ML is definitely a challenge, but it’s also something I’m passionate about. Given the rapid pace of innovation in this field, it’s crucial to stay informed about new developments, and I make it a priority in my work. I regularly read research papers, attend key industry conferences, and actively engage with the AI/ML community—whether through online forums, webinars, or networking with peers. This constant learning helps me spot emerging technologies that can offer new opportunities to enhance our work.
For example, advancements in machine learning frameworks like TensorFlow and PyTorch have opened up new possibilities for developing more sophisticated, efficient models. Likewise, the evolution of cloud platforms such as AWS and Azure has made it easier to scale our AI/ML solutions and run them more efficiently, which is critical for us as we work with massive datasets.
But staying current isn’t just about the models themselves. It’s also about how we integrate AI/ML platforms with the broader data ecosystem. I spend a lot of time looking at how we can integrate our AI platforms with external systems, like data warehouses such as Snowflake, and business intelligence (BI) tools like Tableau or Power BI. These integrations help us unlock the full potential of our data by enabling seamless access to insights across the organization, driving better decision-making and improving the overall user experience.
The key is always to ensure that these advancements can be incorporated into our existing infrastructure in a way that adds value without disrupting the stability or performance of our systems. It’s an ongoing process of evaluation, testing, and iteration, but it’s essential for staying at the cutting edge and ensuring that we’re building the most effective AI solutions for our business.
David: As a leader, what do you think is the most important quality for building and leading high-performing teams?
Padmajeet Mhaske: For me, the most important quality in building and leading high-performing teams is creating an environment of trust, empowerment, and continuous growth. I strongly believe that when team members feel trusted to take ownership of their work and know their ideas are valued, it drives both individual and collective motivation. Encouraging open communication and active collaboration is key, because innovation often stems from diverse perspectives and the free exchange of ideas.
In AI/ML, where the field is evolving rapidly, it’s critical to foster a culture of continuous learning. I make it a priority to create opportunities for my team to stay ahead of the curve—whether that’s through workshops, certifications, hackathons, or side projects. These not only help them sharpen their skills but also inspire creativity and out-of-the-box thinking, which are essential in a field as dynamic as AI.
But empowerment and learning are just part of the equation. A high-performing team is also about alignment—ensuring that everyone is working toward a shared vision and a common set of goals. I make sure my team understands how their work directly contributes to the broader business objectives, which gives them a sense of purpose and drives their commitment to excellence.
Ultimately, what makes a team high-performing isn’t just technical expertise; it’s the ability to work cohesively, stay adaptable, and continuously evolve as individuals and as a group. Leadership, for me, is about creating the conditions where that kind of team can thrive.
David: It sounds like you’re very invested in the growth of your team. What advice would you give to young professionals looking to enter the field of AI/ML?
Padmajeet Mhaske: My advice to young professionals looking to enter the field of AI/ML would be to start by building a strong foundation in both the theoretical and practical aspects of the field. Understanding the core principles—such as the mathematics behind machine learning algorithms, statistical methods, and data structures—is essential because this knowledge will guide you when you encounter more complex models and real-world problems.
However, it’s just as important to get hands-on experience. Theory without practice can only take you so far. Try to build projects that challenge you and solve real-world problems. Participate in hackathons, contribute to open-source communities, or even collaborate on projects with peers. These experiences will not only help solidify your technical skills but also expose you to new tools, frameworks, and ways of thinking.
Networking is another crucial piece of the puzzle. Don’t hesitate to reach out to industry professionals, attend meetups, or get involved in online communities. You never know where a simple conversation or mentorship opportunity might lead. Building relationships in the AI/ML community can provide invaluable insights and open doors for career opportunities.
Lastly, and perhaps most importantly, stay curious. AI/ML is a rapidly evolving field, and the key to success is a mindset of continuous learning. Whether it’s new techniques, emerging tools, or industry trends, there’s always something new to discover. A willingness to adapt, experiment, and embrace challenges will set you apart from others in the field.
David: Great advice. Lastly, what do you find most rewarding about your work at JPMorgan Chase?
Padmajeet Mhaske: For me, the most rewarding aspect of my work at JPMorgan Chase is seeing the tangible, real-world impact of the AI/ML solutions we build. It’s incredibly gratifying to know that the work we do is driving meaningful change—whether it’s enhancing the security of financial transactions, improving the overall customer experience, or streamlining internal processes to increase efficiency. The ability to use AI to solve complex, real-world problems in the financial sector is something that keeps me motivated every day.
Equally fulfilling is the opportunity to mentor and guide younger team members. Watching them grow from early-career professionals to experts in their own right is a source of great pride. It’s rewarding not just to build cutting-edge technology but to help others build their careers and achieve their potential.
Additionally, working in a dynamic, fast-paced environment like this keeps me on my toes and challenges me every day. There’s a constant push to innovate, and that’s exciting. At the same time, it’s incredibly satisfying to know that the solutions we create are shaping the future of AI in the finance industry. We’re not just working on individual projects, but contributing to a broader transformation in how financial institutions leverage AI to deliver better services and drive long-term value.
Ultimately, the combination of seeing our AI-driven solutions make a difference, mentoring the next generation of talent, and playing a part in transforming the financial sector is what makes my work at JPMorgan Chase so rewarding.
David: Thank you, Padmajeet, for sharing your insights with us today. It’s been a pleasure hearing about your journey and your perspective on the future of AI/ML.
Padmajeet Mhaske: Thank you, David. It’s been a pleasure speaking with you today and sharing my journey in AI/ML. I’m always excited to discuss how this field is evolving and the incredible opportunities it presents. AI is transforming industries, and being a part of that change—especially in finance—is both rewarding and inspiring. I truly appreciate the opportunity to share my experiences, and I’m looking forward to the continued growth and innovation we’ll see in this space.
