Welcome to another episode of Leading with Data! This episode is all about Mathangi Sri Ramachandran, a data science leader with over 19 years of experience. Renowned for her work in building cutting-edge solutions and high-performing teams, Mathangi’s insights will illuminate the evolving landscape of data science and AI, not just in boardrooms but for everyone interested in this exciting field. Let’s dive in and discover what’s happening in the Data world with Mathangi!
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Key Insights from our Conversation with Mathangi Sri Ramachandran
- The transition from human-led, data-assisted decision-making to AI-led, human-governed processes marks a significant shift in the data science landscape.
- Continuous learning and adapting to new technologies are crucial for professionals in the field of data science.
- Writing books on data science can serve as a powerful tool for professionals to structure and deepen their knowledge.
- Generative AI has the potential to revolutionize the BFSI sector, particularly in underwriting and collections.
- Diversity in AI leadership is essential, and organizations must embrace different leadership styles to foster an inclusive environment.
- Women in leadership roles should remain true to themselves to inspire and encourage more women to pursue careers in technology and leadership.
Now, let’s look at the details of our conversation with Mathangi Sri Ramachandran!
How do you perceive the evolution of Data Science and AI in the boardroom conversations?
In my two decades of experience, I’ve witnessed a monumental shift in the perception of data science. Initially, data was seen as a tool for static analysis, but today, it’s a game-changer in decision-making. Boardroom conversations have become AI-oriented, with AI no longer just getting a seat at the table but being a central topic of discussion. The potential of AI is immense, and we’re just beginning to scratch the surface. We’ve moved from human-led, data-assisted decision-making to AI-led, human-governed processes, which is a significant transformation.
Reflecting on your journey, how did you keep up with the rapid changes in data science?
The core principles of any job remain the same: sincerity, passion, and hard work. Transitioning from statistics to machine learning and AI was a learning curve, but my ability to read and understand data helped me adapt. I learned by doing, by being part of a team, and by practicing coding, which has always been a stress buster for me. The stress of continuous learning in technology is real, and it’s crucial to stay updated to do justice to your profession and the people you work with.
What inspired you to write your books on data science?
Writing books was a way for me to deepen my understanding of the field. My first book on text mining was about solidifying my knowledge in a structured way. The second book aimed to bridge the gap for non-data science professionals who engage with data science for critical decisions. It’s about setting the right expectations and understanding that data science is not just about engineering or business; it’s a blend that requires a deep understanding of data, machine learning foundations, and the ability to integrate data science into mainstream business.
Can you share insights into your role as Chief Data Officer at YUBI and the impact of data science in lending?
At YUBI, we’re building a robust lending infrastructure that results in financial inclusion. My role spans from data instrumentation to data governance. We’ve mapped AI across the customer’s lending journey, from underwriting scores to document parsing using NLP and vision, to monitoring signals post-disbursement, and optimizing collections strategies. We’re leveraging AI in vision, voice, text, and structured data, backed by a strong data management layer, to drive data through AI and achieve our vision of financial inclusion.
How do you see generative AI impacting the BFSI sector in the next few years?
Generative AI will significantly impact two main areas in BFSI: funding loans and collecting them. We’re focusing on generating credit information reports using generative AI, which can revolutionize underwriting by providing detailed, multi-page financial summaries. In collections, we’re enhancing customer interactions through digital channels like SMS, IVR, and conversation engines in local languages. Generative AI will also transform document processing, marketing campaigns, and customer interactions in banks and insurance companies.
What are your thoughts on diversity in AI and the role of women in leadership?
Diversity in AI is not just about filling quotas; it’s about accepting and accommodating different leadership styles. Organizations need to embrace psychological diversity and respect the diversity of thoughts. Women leaders should be unabashedly themselves and pave the way for more women to enter the workforce and ascend to leadership roles. It’s about creating an environment where diverse perspectives are valued and contribute to a healthy organization.
Summing Up
As we wrap up this insightful conversation with Mathangi Sri Ramachandran, it’s clear that data science and AI are on a journey of continuous transformation. The progress has been astounding, from its beginnings as a tool for analysis to its current role as a powerful force in shaping decisions, like detecting fraud in financial services. Mathangi’s invaluable insights shed light on AI’s transformative power and its critical role in shaping industries. As we explore data science further, let’s learn, adapt, and embrace diversity, just as Mathangi suggests.
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