Data Science and AI technology are being used to create personalized medicine strategies, accelerate the methods for the development of new drugs, and create personalized engagement strategies for raising awareness for new products.
The biggest obstacles facing data science in Biopharma Research are varying data quality in different global regions, lagging regulatory infrastructure, talent acquisition, and lack of data culture.
A sense of personal autonomy and how individual work contributes to the larger organizational goals are major components to creating an effective data culture
The biggest asset a data scientist can have is good problem solving skills. Forget about data science or its technical aspects. Often I find that the true value of a data scientist in companies where data science is primarily used as a decision support tool, is understanding the context in which decisions are being made. And then formulate that into some sort of framework that can be improved by the use of an algorithm oran intelligent automation. This makes problem solving a key component of a high-performing data scientist.
Much of the data that we work with has already been collected and we have very limited input into the collection methods. Once we have it, we are able to evaluate it for bias and consider the downstream implications. There's always going to be bias in the data as long as there is some sort of variance. To mitigate its impact, you have to consider and identify potential biases from the outset, think through their implications, and develop frameworks to measure and account for them? This bias consideration should be a part of every data scientist’s toolkit.
About Suman Giri
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