The Evolving Role of Data Scientists, from Data to Solutions
The nature of work for AI scientists is changing. In some ways, it has made it better. The repetitive, tedious tasks can be automated. Rishi Swami, Head of Data Science at Nirvana Insurance, explained how the transformation of this breed of scientists is taking place at MLDS 2024. Driven by the swift advances in artificial intelligence, he said, “We have seen a lot of innovation in the field of AI, and the pace at which it is coming is immense.” The acceleration, he noted, is not only introducing efficiencies but is also demanding rapid adaptation from data scientists to stay abreast of emerging technologies. Rishi emphasised the practical applications of specific AI tools, stating, “We are seeing a huge impact in practical applications of this.” This observation shows the need for data professionals to broaden their skill sets, moving beyond traditional data science roles. The shift in AI applications, now accessible through open-source APIs and Foundation Models, has dramatically reduced the time and resources needed for AI tasks. This evolution from the traditional data science roles to the new generation AI Engineers can be attributed to the rapid innovation in AI. This evolution is marked by the evolution of data science techniques over time. Rishi traced this progression from early analytical methods to the complex AI models used today. The engineers are also required to tackle business problems, Rishi pointed out, “As a data scientist you’re asked to look at the data and create value out of it.” This shift from technical roles to more solution-oriented positions reflects the increasing demand for data scientists to integrate their skills with business acumen. Addressing the future of work, the talk highlighted potential changes due to AI advancements. It emphasised the importance of understanding the entire data science process, from data collection to model application, in this new landscape. He also discussed the use of AI in automating repetitive tasks like data cleaning and the application of AI tools in feature selection for models, indicating a shift in the skill sets required for future job roles.
This evolution demands a broader skill set from technical to business understanding.
This evolution demands a broader skill set from technical to business understanding.
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["data science", "API", "artificial intelligence", "programming_languages:R", "AI", "innovation", "ML", "foundation models", "R"]
["AI", "artificial intelligence", "ML", "data science", "foundation models", "R", "API", "innovation", "programming_languages:R"]
K L Krithika is a tech journalist at AIM. Apart from writing tech news, she enjoys reading sci-fi and pondering the impossible technologies, trying not to confuse it with reality.