In this Lead with data episode, explore the analytical landscape with Dr. Suati Jain, a seasoned leader with more than two decades of experience. From her unforeseen foray into analytics to leading EXL Analytics’ business in India, Dr. Jain imparts invaluable insights into the ever-evolving world of data science. Continue reading to learn more about her career, her leadership philosophy, and the emerging trends shaping the future of the industry.
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Key insights from our conversation with Dr. Suati Jain
- Intellectual curiosity fuels successful analytical careers.
- Adaptability and continuous learning are essential to navigate various domains of data science.
- Data science leaders excel at deeply understanding problems, collaborating with passionate teams, and simplifying solutions.
- Post-COVID, a systematic approach prioritizes building data infrastructures, emerging as a major trend in the industry.
- The imminent widespread status of generative ai promises diverse applications across industries.
- Continuous learning and technological updates are imperative for those venturing into careers in data science or generative ai.
- Coding is only one facet; Careers in data science require a broad set of skills, including domain expertise and project management.
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Now, let’s discuss Dr. Swati Jain’s answers to some of the essential questions about ai!
How did your journey in analytics begin?
I approached life without a predetermined plan to enter the world of analytics, but always looking for an intellectually stimulating career. My academic background in economics, along with an internship at DSP Merrill Lynch, where I worked on innovations in the Indian debt market, laid the foundation for my interest in research and analysis. I turned down a sales job offer from DSP Merrill Lynch to pursue something intellectually engaging. This choice led me to pursue a PhD while working at Ernst & Young, where I delved deeper into statistical and pricing analysis, marking the beginning of my journey with data and numbers.
What were the first days of your career like and how did you adapt to different fields?
The first few days involved a lot of learning and adaptation. I went from content creation for a legal firm to financial analysis in transfer pricing at EY, and then to market research in the pharmaceutical industry. Each domain was different and required in-depth knowledge of the respective fields. The key was to stay focused on the core goals, regardless of the size of the data, and extract meaningful insights. My diverse experience in different domains has helped me become more adaptable and adept at leveraging data for various analytical purposes.
As a leader, how has your perspective evolved over the years?
My leadership perspective has evolved to prioritize deep understanding of problems, collaborative research, and working with a passionate team to formulate optimal solutions. Emphasizing simplicity in communication with stakeholders ensures successful adoption, focusing on starting with the end in mind. Critical considerations include evaluating the impact of the solution and ensuring accurate consideration of key variables to avoid major oversights.
What are the current trends and conversations with customers in the data science industry?
Post-COVID, customers are prioritizing analytics, initially focusing on building data infrastructures like warehouses. The demand for data engineers remains high due to their crucial role in preparing data for generative ai (JennyAI). Customer discussions now focus on digital transformation and implementing generative ai across applications, encompassing content extraction, classification and summarization.
<h2 class="wp-block-heading" id="h-how-do-you-see-the-role-of-generative-ai-in-the-industry-s-future”>How do you see the role of generative ai in the future of the industry?
Generative ai is becoming mainstream and I believe it will be integrated into various use cases and become as ubiquitous as Google is today for information search. It will be used for automation, creation and generation in all industries. As the technology matures, we will see more implementations and the industry will learn where it is most effective. It is essential that individuals and organizations start using generative ai to their advantage to stay ahead in their respective fields.
<h2 class="wp-block-heading" id="h-what-advice-would-you-give-to-someone-starting-their-career-in-data-science-or-generative-ai“>What advice would you give to someone starting their career in data science or generative ai?
First, look within and identify what fascinates you personally. Decide on the industry or domain you want to be in and then educate yourself in the data and analytics space. Remember that implementing an ai project involves more than just coding; requires domain understanding, project management, and several other skills. Develop a passion for continuous learning and discipline yourself to learn something new every day. This approach will go a long way toward building a successful career in this ever-evolving industry.
summarizing
Dr. Swati Jain’s narrative reveals the evolution of analytics, emphasizing adaptability, nuances of leadership, and emerging trends. As data science positions itself for systematic growth, her perspectives on GenAI and continuous learning resonate as guiding principles for aspiring professionals. This insightful dialogue with a seasoned analytics expert illuminates paths to success in the evolving data science landscape.
For more interesting sessions on ai, data science and GenAI, stay tuned to Leading with Data.
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