Dive into the transformative world of data science with Analytics Vidhya’s innovative Leading With Data series. In this exclusive interview in the series, Kunal Jain, CEO of Analytics Vidhya, engages in a fascinating conversation with Vin Vashishta, a distinguished ai leader. He uncovers the secrets of Vin’s journey, marked by a strategic shift from technical to leadership roles, while sharing invaluable knowledge and experiences.
Let us begin!
Key ideas
- Embark on Vin Vashishta’s extraordinary journey from installing PCs to becoming a pioneer in ai strategy.
- Discover his perspective on making critical decisions for leaders: balancing quick solutions with the reliability of data science applications.
- Learn about Vin’s unique process for forecasting industry trends before they explode, guiding your strategic moves in an ever-evolving landscape.
- Explore the genesis of your startup and witness its evolution over the years, offering a first-hand account of the challenges and triumphs.
- Delve into Vin’s belief in the importance of business vision, even for late adopters of cutting-edge technologies, as a driving force for sustained success.
- Understand why Vin advocates for technical experts to branch out into various domains, emphasizing the need to advance in a rapidly advancing field.
How did you embark on your journey into data science?
I began my studies to dedicate myself to civil engineering, following in my father’s footsteps. However, my first encounter with programming at age 12 had a profound impact on me. I was captivated by the ability to create something in a virtual environment. I took a programming class during my freshman year of college and immediately knew it was my passion. My focus shifted to programming, which was around 1994-95. My journey into data science was not easy. I graduated during the first ai hype cycle in the 90s. Despite my aspirations to work for Microsoft and create advanced models, I was in more traditional software engineering roles. I worked my way up from PC installation to website creation and database administration. My first corporate job involved installing software and platforms internally and working directly with clients. This experience was crucial as it taught me the importance of delivering on software promises.
What were the first 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 that we have today. I built models in several languages, including C, C++, and Java, because we had to optimize everything due to technological limitations. We didn’t have the cloud infrastructure we have now and data at scale was only available to massive enterprises. My first clients were large companies and it wasn’t until around 2016 that small and medium-sized companies started approaching me. Working with these smaller clients introduced me to real-world constraints such as budget and time, a different experience than 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 how to balance value delivery with technical rigor propelled me from technical roles into leadership and strategy.
Social media, particularly Twitter and later LinkedIn, played an important role in the expansion of my business. It changed my sales funnel completely, increasing the number of inquiries and opportunities. I found a unique voice speaking about data science and machine learning from an executive perspective, which set me apart. My brand has always been about pragmatism, about discussing what works in the field and what doesn’t, based on my work and my daily experiences.
<h2 class="wp-block-heading" id="h-what-does-your-current-role-as-an-ai-advisor-entail”>What does your current role as an ai advisor entail?
Currently my role is mainly advisory. Previous clients or colleagues often invite me to take calls, answer questions, and explain technical concepts related to monetization for businesses. For example, when Apple announced its new silicon, I sent out a newsletter explaining the importance of performing 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 businesses and how to turn it into a value story.
<h2 class="wp-block-heading" id="h-what-are-your-thoughts-on-the-future-of-data-science-and-generative-ai“>What do you think about 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 while I didn’t predict the exact impact of ChatGPT, I knew where we were headed. The challenge is not only to have the vision, but also to be able to convince companies to prepare and adopt these technologies.
What advice do you have for data scientists transitioning into new roles?
I advise you to recognize when you have reached a technical impasse and focus on multiplier skills that improve the team and the organization. Instead of continually learning new technical skills, develop capabilities to improve everyone around you. This could mean moving into roles such as director, 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 most difficult thing I have ever experienced, but it was a great experience. My book has received mixed reactions, with some technical professionals finding it lacking in code and implementations. However, it has found its niche among sales teams, C-level executives, and specialized professionals looking to transition into strategic roles. The book focuses on creating value with data science, not just delivering more technology.
What are you most excited about in 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 this field to grow. Data science is unique because it can deliver on its promises, and I look forward to seeing this evolution.
summarizing
From dealing 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 advances into tangible business value.
Stay tuned with us on Lead with data for more inspiring data talks. See you next week with another exciting episode!
