Mastering the Art of Data Science Strategy: A Conversation with AI Visionary Vin Vashishta
Dive into the transformative world of data science with Analytics Vidhya’s groundbreaking series Leading With Data. In this exclusive interview from the series, Kunal Jain, CEO of Analytics Vidhya, engages in a riveting conversation with Vin Vashishta, a distinguished AI leader. Unveil the secrets of Vin’s journey, marked by a strategic shift from technical roles to leadership, as he shares invaluable insights and experiences.
Let’s get started!
- Embark on Vin Vashishta’s extraordinary journey, transitioning from installing PCs to becoming a trailblazer in AI strategy.
- Uncover his perspective on critical decision-making for leaders: balancing quick solutions with the reliability of Data Science applications.
- Gain insights into Vin’s unique process of foreseeing industry trends before they explode, guiding his strategic moves in the ever-evolving landscape.
- Explore the genesis of his startup and witness its evolution over the years, offering a firsthand account of the challenges and triumphs.
- Delve into Vin’s belief in the significance of a business vision, even for late adopters of cutting-edge technologies, as a driving force for sustained success.
- Understand why Vin advocates for technical experts branching into diverse domains, emphasizing the necessity for forward movement in the rapidly advancing field.
How did you Embark on your Data Science Journey?
I started my education to go into civil engineering, following in my father’s footsteps. However, my first encounter with programming at 12 profoundly impacted me. I was captivated by the ability to create something in a virtual environment. Took a programming class during my first year of college and immediately knew it was my passion. My focus switched to programming, which was around 1994-95. My journey into data science was not straightforward. I graduated during the first AI hype cycle in the ’90s. Despite my aspirations to work for Microsoft and build advanced models, I was in more traditional software engineering roles. I worked my way up from installing PCs to building websites and database administration. My first corporate job involved installing software and platforms in-house and working directly with clients. This experience was crucial as it taught me the importance of delivering on software promises.
What were the Early Challenges you Faced with Data Science Models?
My first data science project was in 2012, and back then, we didn’t have the libraries and resources we have today. I built models in various languages, including C, C++, and Java, because we had to optimize everything due to technology limitations. We didn’t have the cloud infrastructure we have now, and data at scale was only available to massive companies. My early clients were large companies, and it wasn’t until around 2016 that small and midsize businesses began to approach me. Working with these smaller clients introduced me to real-world constraints like budget and time, a different experience from the corporate world.
How did you Transition from Technical roles to Strategy and Leadership?
After being laid off in 2012, I quickly turned my side consulting practice into a full-time business, V Squared. Initially, my work was more BI analytics than data science. As the field evolved, I began building statistical models and working with scientists who taught me the importance of model explainability. This experience led me to bridge the gap between traditional machine-learning approaches and the rigorous standards of science. I learned to discern when a quick and more reliable solution was necessary. This understanding of balancing value delivery with technical rigor propelled me from technical roles into leadership and strategy.
How did Social Media Influence your Business Growth?
Social media, particularly Twitter and later LinkedIn, played a significant role in expanding my business. It changed my sales funnel completely, increasing the number of inquiries and opportunities. I found a unique voice by discussing data science and machine learning from an executive perspective, which set me apart. My brand has always been about pragmatism, discussing what works in the field and what doesn’t, based on my daily work and experiences.
What does your Current Role as an AI Advisor Entail?
Nowadays, my role is primarily advisory. Past clients or colleagues often bring me in to sit in on calls, answer questions, and explain technical concepts regarding monetization for businesses. For example, when Apple announced its new silicon, I sent a newsletter explaining the significance of running inference on a watch and what it means for IoT. My job is to help C-level leaders understand the implications of technology for their business and how to turn it into a value story.
What are your Thoughts on the Future of Data Science and Generative AI?
I believe data science has the potential to live up to its hype because it works and delivers on its promises. I saw the potential of generative models like GPT early on, and although I didn’t predict the exact impact of ChatGPT, I knew the direction we were headed. The challenge is not just having the vision but also being able to convince businesses to prepare for and adopt these technologies.
What Advice do you have for Data Scientists Transitioning into New Roles?
I advise you to recognize when you’ve hit a technical plateau and focus on multiplier skills that improve the team and organization. Instead of continuously learning new technical skills, develop capabilities to enhance everyone around you. This could mean transitioning into roles like principal, staff, or distinguished data scientist or moving into leadership, product management, or strategy. When you feel bored or trapped, consider becoming a multiplier to reignite your passion and help others grow.
Can you share Some Insights from your Book and your Experience as an Author?
Writing a book was the hardest thing ever, but it was a great experience. My book has received mixed reactions, with some technical practitioners finding it lacking in code and implementations. However, it has found its niche with sales teams, C-level executives, and specialized practitioners looking to transition into strategy roles. The book focuses on creating value with data science, not just delivering more technology.
What Excites you most about the Next Few Years in Data Science?
I’m excited to see the field mature. We now have senior data scientists with leadership experience who are forcing the field to grow. Data science is unique in that it can deliver on its promises, and I’m looking forward to watching this evolution.
From grappling with early challenges in model development to harnessing the power of social media for business growth, Vin’s story is a testament to resilience and adaptability. As an AI Advisor, he emphasizes the crucial role of translating technical advancements into tangible business value.
Stay tuned with us on Leading with Data for more such inspirational data talks. See you next week with another exciting episode!