Mercury Data Science + Janssen abstract publication at the American College of Neuropsychopharmacology 61st Annual Meeting
“Mercury Data Science has taken a tool it originally developed for COVID-19 research and applied it into new areas of research and innovation.”
Most data is raw and needs to be shaped into usable features that highlight signal and minimize noise. “Feature engineering” is the most important part of real-world data science, paving the path from raw data to valuable insights.
Why companies need to think ahead about infrastructure for data science.
Domain and technical knowledge aren’t sufficient criteria when looking for your new data science hire. We have found that a better characterization of a data scientist’s innate potential is the way they balance creativity and skepticism.
How to review and gain insights from the mountain of medical literature without reading it.
Accelerate hypothesis development with AI-driven insights from scientific literature.
We are constantly sending signals that communicate emotion, intent, and even underlying medical conditions.
Data science, done correctly, can help teams avoid common pitfalls.
We expect AI to create $13 trillion of GDP growth by 2030 across industries such as manufacturing, agriculture, energy, and pharma.
The expectation that AI applications should all be glamorous can cause people to miss the considerable impact that Data Science is having now (on companies that embrace it).
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