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An inefficient clinical trial will result in wasted resources, delays in new treatment for patients in need and negatively impact population health even though we have the scientific understanding to do much better. The mission of Lindus Health is to accelerate clinical trials with a vision to fix the system. Working towards such a cause with a team of brilliant people who are as excited about this mission as I am makes working at Lindus Health an amazing opportunity.
Lindus Health places huge emphasis on R&D. Innovative research is a must to break free from the traditions anchored in this field and one of the company goals is to generate innovative solutions to long standing problems. As a machine learning engineer, I can see the potential of using machine learning to give meaning to past data and design the patient-centric, efficient clinical trials of the future. The clinical trials field features many large datasets (including publicly available ones like clinicaltrials.gov), which have been relatively under-explored. There are challenges associated with dealing with a mix of structured and unstructured data, but I’ve been able to overcome these and build predictive models for an improved trial design process. There is a lot of room for innovation in this field and it is a great professional motivation for me to work towards this at Lindus Health.
In addition to working as a machine learning engineer, I have a design background where I believe some of my most valuable skills originated from. At Lindus Health everyone’s skills and contributions are valued and appreciated which is one of the reasons that I could immediately see myself as a fit to this company. I can contribute to ongoing product challenges from a designer’s perspective as well as being highly focused on a machine learning project. In my opinion, this is one of the perks of working in a growing start-up environment with open-minded people where you can contribute to different business aspects instead of focusing only on one thing that you can do.
Ece Kavalci, Machine Learning Engineer
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