Dr. Satyajit Wattamwar, Head of Data Science at Unilever, shares his journey from chemical engineering to data-driven leadership. He discusses the significance of contextual understanding in data science and how Unilever navigates complex datasets. The conversation delves into AI's dual role in enhancing operational efficiency and fostering innovation. Dr. Wattamwar highlights key pitfalls in data science, the critical amount of data needed for effective models, and the evolving landscape that necessitates continuous learning for career growth in an AI-driven world.
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insights INSIGHT
Physicists in Data Science
Physicists often work with large datasets and develop algorithms, making them suited for data science.
Their combined knowledge of physics, math, and algorithms makes them valuable in this field.
insights INSIGHT
Data Relevance at Unilever
Large datasets are valuable, but their relevance depends on the specific problem.
Unilever leverages diverse data for various use cases, finding value even in smaller, focused datasets.
insights INSIGHT
Data Quantity and Time Constraints
The necessary amount of data depends on the problem's context and the system's time constant.
Understanding the relevant history influencing the current state is crucial for effective modeling.
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Satya unpacks how Unilever utilizes its database to inform its models and how to determine the right amount of data needed to solve complex problems. Dr. Wattamwar explains why contextual problem-solving is vital, the notion of time constraints in data science, the system point of view of modeling, and how Unilever incorporates AI into its models. Gain insights into how AI can increase operational efficiency, exciting trends in the AI space, how AI makes experimentation accessible, and more! Tune in to learn about the power of data science and AI with Dr. Satyajit Wattamwar.
Key Points From This Episode:
Background on Dr. Wattamwar, his PhD research, and data science expertise.
Unpacking some of the commonalities between data science and physics.
Why the outcome of using significantly large data sets depends on the situation.
The minimum amount of data needed to make meaningful and quality models.
Examples of the common mistakes and pitfalls that data scientists make.
How Unilever works with partner organizations to integrate AI into its models.
Ways that Dr. Wattamwar uses AI-based tools to increase his productivity.
The difference between using AI for innovation versus operational efficiency.
Insight into the shifting data science landscape and advice for budding data scientists.
Quotes:
“Around – 30 or 40 years ago, people started realizing the importance of data-driven modeling because you can never capture physics perfectly in an equation.” — Dr. Satyajit Wattamwar [0:03:10]
“Having large volumes of data which are less related with each other is a different thing than a large volume of data for one problem.” — Dr. Satyajit Wattamwar [0:09:12]
“More data [does] not always lead to good quality models. Unless it is for the same use-case.” — Dr. Satyajit Wattamwar [0:11:56]
“If somebody is looking [to] grow in their career ladder, then it's not about one's own interest.” — Dr. Satyajit Wattamwar [0:24:07]
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