Samuel Konstanty
About Samuel Konstanty
Samuel Konstanty is a Data Scientist with a Master's degree in Computer Science from The University of Texas at Dallas. He specializes in Natural Language Processing and currently works at Loopback Analytics, where he develops data solutions for health systems and specialty pharmacies.
Current Role at Loopback Analytics
Samuel Konstanty serves as a Data Scientist at Loopback Analytics since 2021. In this role, he focuses on utilizing advanced data analysis techniques to support health systems and specialty pharmacies. His work involves developing data pipelines and employing Natural Language Processing (NLP) to extract valuable insights from unstructured clinical notes, contributing to Real World Data studies.
Previous Experience at Loopback Analytics
Prior to his current position, Samuel held the role of Solutions Data Analyst at Loopback Analytics from 2019 to 2021. In this capacity, he was responsible for analyzing data solutions and supporting various projects aimed at improving healthcare outcomes. Additionally, he completed a Data Science and Machine Learning internship at Loopback Analytics in 2018, where he gained practical experience in data analysis and machine learning applications.
Education and Expertise
Samuel Konstanty earned a Master's degree in Computer Science from The University of Texas at Dallas, studying from 2017 to 2019. He also holds a Bachelor's degree in Biomedical/Medical Engineering from the same institution, completed from 2013 to 2017. His academic background provides a strong foundation in data science, machine learning, and biomedical engineering, equipping him with the skills necessary for his current role.
Leadership in Therapy Specific Outcomes Coalition
Samuel serves as the Lead Architect for the Therapy Specific Outcomes Coalition initiative. This initiative aims to unite large academic medical centers to develop clinical endpoints for complex disease states. His leadership in this project highlights his commitment to advancing healthcare research and improving clinical outcomes through collaborative efforts.
Technical Skills and Tools
Samuel utilizes a range of technical tools and programming languages in his work. He is proficient in Pyspark, Databricks, SQL, and Power BI, which he employs to provide data-driven solutions for health systems and specialty pharmacies. His expertise in these technologies enables him to effectively process and analyze large datasets, contributing to informed decision-making in healthcare.