01Overview
About careerzynith careerzynith stands at the forefront of healthcare innovation, where data meets clinical discovery to transform patient outcomes. As a leading player in the pharmaceutical and healthcare analytics landscape, careerzynith is committed to advancing medical research through the power of real-world evidence (RWE) and cutting-edge data science. Our mission is to bridge the gap between clinical trials and real-world healthcare data, enabling biopharmaceutical companies, healthcare providers, and research institutions to make informed decisions that improve patient care. With a strong foundation in healthcare analytics, careerzynith has established itself as a trusted partner in the clinical research ecosystem. Our team of data scientists, clinical researchers, and healthcare professionals work collaboratively to unlock insights from complex healthcare data, driving innovation in treatment patterns, patient outcomes, and healthcare economic modeling. When you join careerzynith, you become part of a dynamic organization that values intellectual curiosity, technical excellence, and a passion for improving healthcare outcomes. We foster a culture of continuous learning, innovation, and collaboration, where your contributions directly impact the future of healthcare analytics and clinical research. The Role Real-World Evidence Data Scientist careerzynith is seeking an experienced Real-World Evidence Data Scientist to join our Scientific Affairs team. In this pivotal role, you will be responsible for generating Real-World Evidence (RWE) through advanced analysis and innovative methodologies in the clinical trials space. As a key member of our Scientific Affairs group, you will influence internal and external decision-making through the application of innovative data science and technology. This position offers a unique opportunity to work at the intersection of healthcare, data science, and clinical research, contributing to meaningful advancements in medical treatments and patient care. Your work will directly impact how biopharmaceutical companies understand treatment effectiveness, patient populations, and healthcare economic burdens. Key Responsibilities Data Mining and Analysis Mine raw data sets to deliver standardized, analysis-ready patient cohorts from in-house data resources or licensed platforms, analyze and interpret results, and assist in shaping analysis strategy. Cross-Functional Collaboration Partner with members of the careerzynith Clinical Trial teams to deliver meaningful insights from real-world, clinical, and other data sources to support biopharmaceutical RWE and clinical trial requests. Potential initiatives include analysis of unmet needs, safe and effective treatment in specific disease states, treatment patterns and healthcare economic burden, and cost-effectiveness modeling. Data Management Clean, transform, and store data into an integrated data lake for future analysis and broader team utilization. Statistical Analysis Perform data mining and statistical analysis to analyze RWE/RWD to discover new insights regarding patient populations and treatment outcomes. Platform Development Support the development of real-world data, analytics, and platform capabilities, applying advanced analytics and tools to address scientific questions from biopharmaceutical companies. Research Study Design Collaborate on the design and execution of outcomes research studies. Quality Assurance Ensure high quality, comprehensive, and readily interpretable results from RWE studies and analyses. Visualization and Reporting Build dashboards, analytics, and reports for internal and external use. Data Vendor Management Conduct due diligence on new real-world data provider vendors and drive RWD quality evaluations to inform project decisions. Essential Qualifications Experience Demonstrated experience in designing and executing robust and reproducible statistical analyses in the context of epidemiology and observational research with healthcare data. Statistical Knowledge Strong knowledge of common statistical testing methods including generalized linear models, clustering and discrimination, decision trees, unsupervised learning, time series analysis, and survival analysis. Data Handling Experience handling large data from diverse data sources. Data Privacy Experience handling protected health information (PHI) with strict adherence to privacy regulations. Technical Skills Proficiency in at least one of the following Tableau, Power BI, Alteryx, Spotfire, or other business intelligence tools. Preferred Qualifications Education PhD or Master's degree in statistics, biostatistics, computer science, mathematics, systems engineering, biomedical engineering, or related field. Healthcare IT Expertise Expertise in EMR/Health IT, disease registries, and insurance claims databases. Clinical Terminology Proficiency in clinical data standards, clinical terminologies, and controlled vocabularies used in .
