An experienced data scientist is critical when you’re looking to build tools and technologies that are tackling complex problems in healthcare. We pride ourselves on being data-driven; consequently, the impact of the data team at NeuroFlow is felt throughout every department. In this latest version of our “meet the team” series, Rishi Patel, NeuroFlow’s Data Scientist, describes the twists and turns that led him to join the NeuroFlow team last year. In this interview, Rishi takes a look back on his previous days at CERN, his motivation for jumping into digital health, and shares what has him excited about the days ahead for the company!
Q: Can you talk a bit about your professional path that led you to NeuroFlow?
Data Scientist Rishi Patel
By training and education, I am a physicist who has worked professionally at the CERN Large Hadron Collider (In Geneva, Switzerland) and Fermilab National Labs (in IL, USA). These experiments produce petabytes of data that needs to be rapidly processed, modeled, and communicated to the greater scientific community. So my training as a physicist went hand in hand training as a data scientist. In short, the same path that led me to being a particle physicist led me to NeuroFlow. I was curious about a problem that I felt was important, and want to work towards finding a solution.
The fork in my professional path started at the beginning of the COVID-19 pandemic. Many scientists at CERN and around the world, thought about what impact we could have on the emerging challenges of the pandemic. One particular challenge is that experts were buried under avalanches of information about public health policies, digital health infrastructure, and possible paths of developing a vaccine. This is also one of the core challenges of big particle physics experiments: how does an experiment store and categorize data, so that it is possible to conduct research without missing anything important? As a side project with a volunteer organization called Science Responds, I used the information processing techniques I learned in my experiment to build an algorithm that organized scientific research publications. Basically, it took stacks of paper, bound them into books, and then organized them into a library.
When I was perusing this library of publications, I found that many papers were about the wave of behavioral health challenges around the world. Given the wealth of knowledge created by scientists around the world (in just a few months), I knew that there must be many people motivated to solve it.
Q: What drew you to transition from academia to the healthcare industry?
Academia and healthcare have an intersection in two keywords: complexity and culture. In physics, complexity can refer to a system where a change in one part can drastically affect the whole system (like changes in the cells of a human body). I think the healthcare industry is also a complex system, which is great! The evolution starts at the cellular level with bold innovations like NeuroFlow. We identify patterns in the system, analyze what works well and what can be improved, and we move fast to fill evolving demands and spread out in the system. In many ways, this reflects the path to discovery in academia, like my PhD thesis on the discovery of the Higgs boson at CERN.
NeuroFlow as a company has grown to include a similar blend of staff from diverse backgrounds bringing new insights to build on the existing company knowledge and expertise. In transitioning from academia to NeuroFlow, I did not think of it as leaving one work environment to join another, but more so shifting the focus of the problems I wanted to solve while preserving the work culture that I love.
Q: What makes a successful data scientist?
Data scientists sometimes need to be like Da Vinci in my opinion. We need the technical expertise to communicate with the engineers to deploy products and identify issues on the platform. Sometimes we need to paint a picture or tell a story with the data to explain how successful a product can be, how well existing content is working, and how we are performing as a company. Other times we have to experiment with new techniques to build products with market sizzle. A major strength of the data team at NeuroFlow is knowing when to fulfill each role and how to prioritize accomplishing many roles at once. This is the most important part of data science as a career, and allows the data team to have the agility to zoom into very detailed problems and zoom out to broader topics.
Q: What has you most excited for NeuroFlow in 2021?
I am most excited to see how core products on the NeuroFlow platform evolve to meet the needs of our healthcare partners. I’ve been working on how we can give a snapshot of patient wellness based on passive and active data they are generating on the platform. With more patients on the platform given our continued growth, we hope this tool can provide more sophisticated patient alerts to ultimately deliver data that is actionable and insightful to care providers.
Interested in joining Rishi and others on the NeuroFlow team? Check out our career opportunities!
