Interview with Dev Goyal, Lead Machine Learning Engineer, HEALTH[at]SCALE Technologies

Dev Goyal
Brief Overview of The Company

HEALTH[at]SCALE was founded in 2015 to solve the problem of bringing precision delivery to precision medicine. We are on a mission to match the world’s patients to the right treatments by the right providers at the right times, at critical points across the care continuum, using specialized advances in predictive machine intelligence.

Our company is led by a world-renowned team of machine learning and clinical faculty out of MIT, Harvard, Stanford and U-Michigan. Our team has provided thought leadership to the healthcare artificial intelligence and machine learning space for the last two decades; and has deep expertise in predictively optimizing complex care decisions both at the individual and the system-level.

HEALTH[at]SCALE’s offerings include scalable and secure software-as-a-service (SaaS) applications that can be hosted on-premise or in the public cloud to deliver predictive precision recommendations at points of care. The company recently secured a $16M Series A to help support our growing team and scale our capabilities out to a larger customer set. HEALTH[at]SCALE was also recognized by Forbes as one of 25 machine learning startups to watch in 2019.


Background and Role at Company

I first began exploring computer science as a Mechanical Engineering student at the National University of Singapore (NUS). After my degree at NUS, I enrolled in the graduate program in Computer Science at the University of Michigan. At U-Michigan, I was advised as a PhD student by Profs. Jenna Wiens (who leads the Machine Learning for Data-Driven Decisions group at U-Michigan) and Zeeshan Syed (formerly a tenured faculty member at U-Michigan and currently the CEO of HEALTH[at]SCALE). I had the opportunity to research the application of machine learning for early prediction of Alzheimer’s disease. Excited at the practical applicability of machine learning to high priority challenges in healthcare, I joined HEALTH[at]SCALE as its first non-founding employee in 2018 after interning with them the previous summer. I currently serve as the Lead Machine Learning Software Engineer of the company.

Through my role at the company, I’ve been centrally involved in the development of solutions that are among the largest deployments to date of machine learning for healthcare use cases, helping payers and providers manage populations comprising tens of millions of individuals in live production settings. These solutions have shown immense promise in their ability to help patients achieve better outcomes and reduce total cost of care.


Company Team

HEALTH[at]SCALE was founded by world-renowned team of machine learning and clinical faculty with ties to U-Michigan, Stanford, MIT and Harvard. We are a team of extraordinary individuals and hire extremely carefully and selectively. Our team shares a deep interest in healthcare and a passion for building technologies that positively affect people’s lives. Along with a strong interest in healthcare, the HEALTH[at]SCALE team has exceptional software engineering and machine learning skills, with training at top CS/ML programs (including UC-Berkeley, MIT, GA-Tech, U-Washington etc.) and experience at large data-intensive tech companies (including Google, EMC/VMWare, Microsoft, IBM etc.).

Vision of Machine Learning in Healthcare
The over-arching goal of machine learning in the healthcare space should be to help patients lead healthier and happier lives while reducing avoidable diseases and complications. A significant challenge unique to healthcare is the problem of patient heterogeneity, i.e., patients having different ‘health profiles’ as a result of their environmental, genetic, biological and a variety of other factors. This makes it especially important for healthcare solutions to be personalized to the specific characteristics of each patient. As technology continues to become more ubiquitous in modern day life in the form of wearables, mobile devices and smart assistants, each of these provide a potential source of patient data as well as an avenue for the precise delivery of personalized healthcare recommendations to patients.


Advice to Others

Machine learning applied to healthcare is a unique field in that it requires mastery of several domains, including software engineering, machine learning as well as clinical know-how. At HEALTH[at]SCALE, successful candidates possess expertise in all of the above areas; however, its equally important to have experience applying machine learning solutions to real world problems that are typically characterized by messy data that often presents in large volumes.


Looking Ahead

At HEALTH[at]SCALE, we are pioneering the precision delivery of healthcare through personalized recommendations to patients of treatments and providers that are likely to improve outcomes, costs, access and satisfaction for them. Moving forward, we aim to continue driving innovations that directly improve healthcare for patients and leverage exciting developments related to machine intelligence, wearables, mobile devices, smart assistants — as some examples —- to drive longer, healthier and happier lives for those around us.