I’m a lead data scientist at NuvoAir. My work focuses on using statistical modeling and machine learning algorithms to create actionable insights for the clinical team to better care for patients. I’m also responsible for implementing and deploying new data products to collect informative data. I collaborate with others at NuvoAir in designing questionnaires, making decisions on data collection, and creating data storage strategies.
Due to the time difference between Europe and Boston, my mornings are usually filled with meetings with the engineering and data science teams. During these meetings, we share updates, new experiences or knowledge learned. Otherwise I spend most of my time implementing machine learning models or generating insights from the data collected. Since I'm relatively new, I am still learning something new everyday about our hardware and software system.
Previously, I was an assistant professor and researcher, focusing on statistical modeling in the genetics of COPD. With the influx of large amounts of digital data generated using wearable devices or other sensors, I got very interested in applying the statistical/ML methods to these data to directly impact people’s health. This was a big transition for me and I felt very lucky that I joined NuvoAir. It gave me the opportunity to realize this vision to improve healthcare or even disease prevention using digital data.
I am very passionate about my work here at NuvoAir, especially the pipeline we are building to utilize the data collected using our products to improve patient care. I also enjoy working with the people here at NuvoAir. We share the same goal and we have a very collaborative, engaging, and positive working environment. People share appreciation for each other every week and through conversations.
There are a lot of differences between NuvoAir and the academic setting I was working in. The task shifts from scientific research and discovery to developing products directly used by the patients. The team I work with here includes people from a variety of disciplines, including engineers, data scientists, clinicians, graphic designers, and many more. It’s very interesting to learn the perspectives of people from different backgrounds.