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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.

Good data science comes from a state of precarious balance between two diametrically opposed traits, skepticism and creativity. Fall too far towards one pole and your work stagnates, paralyzed by uncertainty. Too far in the other direction and you waste resources chasing rainbows.

As our society becomes more reliant on data driven insights, people are shifting careers and going back to school to pursue positions in data science. This influx of new candidates, and the increase in available positions, creates a situation where hiring managers aren’t certain of what to look for and new data scientists aren’t certain where they should focus their training. While knowledge base and proficiencies are important metrics, good candidates can learn on the job to fill in knowledge deficiencies and, more importantly, there is a large gap between knowing facts and the ability to apply these effectively. …

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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.

Act 1: I have my data; now what?

Many companies have begun storing every piece of data they can think of in order to tap into secret insights about their customers, products, processes, and financial metrics. Data collection is the new gold rush, and every piece of data may be mined for useful information. However, the path from raw data to valuable insights isn’t always clear, leaving many companies to collect data without a plan for the future. …

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Speech and video analytics to characterize human behavior

Authors: Jonathan Gallion, Michael Bell

We are constantly sending signals that communicate emotion, intent, and even underlying medical conditions. These signals can take the form of facial macro and micro expressions, variations in posture and body movements, as well as minute fluctuations in vocal pitch, frequency, and pronunciations. AI is now at a point where we can read and interpret these signals for both business and health care purposes.

Ongoing development of digital diagnostics and increased adoption of telemedicine technologies are opening doors to extract visual, auditory, and text-based biomarkers that point toward diagnoses, both mental and physical, to improve patient access and outcomes. Beyond healthcare, the current acceleration in the adoption of video-based conferencing and recruiting also creates the opportunity for interesting applications and the development of novel datasets outside of medicine and research. …


Jonathan Gallion

Senior data scientist and entrepreneur at Mercury Data Science.

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