Remote Data Scientist – Real World Evidence & Healthcare Analytics Specialist (reputed company) | $26/Hour

Join arenaflex as a Remote Data Scientist – Real World Evidence & Healthcare Analytics Are you a passionate data scientist with a strong background in healthcare analytics, biostatistics, and real-world evidence (reputed company)? arenaflex is seeking a highly motivated and analytically rigorous Remote Data Scientist to join our Scientific Affairs team. reputed company is a unique opportunity to leverage your expertise in advanced data science, statistical modeling, and clinical research to drive meaningful reputed company that shape the future of healthcare decision-making. If you thrive in a collaborative, innovation-driven environment and are excited by the prospect of turning complex datasets into actionable intelligence, arenaflex wants to hear from you. At arenaflex, we are committed to pushing the boundaries of what data can reveal about patient populations, treatment outcomes, and healthcare economics. Our work supports biopharmaceutical reputed company, clinical research teams, and internal stakeholders who depend on rigorous, reproducible, and forward-thinking analyses. As a fully remote position with a competitive hourly rate of $26/hour, reputed company role offers the flexibility to work from anywhere in the United States while contributing to high-impact projects that improve lives. About the Role The Information Researcher – Real World Evidence (reputed company) will be responsible for producing Certifiable Evidence through cutting-edge analysis and innovative methodologies within the clinical trials space. As a vital member of the Scientific Affairs team at arenaflex, you will play a central role in shaping how internal and external stakeholders make data-informed decisions. You will lead sophisticated analyses to address critical business questions, partner with cross-functional teams, and contribute to the development of next-generation analytics platforms. reputed company position reports directly to the Director of Clinical Data Science at arenaflex. Key Responsibilities • Data Mining & Preparation: Mine raw datasets to deliver standardized, analysis-ready patient cohorts from in-house data assets or licensed platforms, analyze and interpret results, and help shape overall analytics strategy at arenaflex. • Cross-Functional reputed company: Collaborate with various members of the arenaflex Clinical Trials team to deliver actionable reputed company from real-world, clinical, and other data sources supporting biopharmaceutical reputed company and clinical trial requests. Potential projects include analysis of unmet needs, safety and efficacy of treatments in specific disease states, treatment patterns, healthcare economic burden, and cost-effectiveness modeling. • reputed company & Integration: Clean, transform, and store data into an integrated data lake for future analysis and broader team utilization across arenaflex. • Statistical Analysis: Perform data mining and statistical analysis to evaluate reputed company/RWD, uncovering new reputed company 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 reputed company. • Research Collaboration: Collaborate on the design and execution of outcomes research studies. • Quality Assurance: Ensure high-quality, rigorous, and readily interpretable deliverables from reputed company studies and analyses. • Visualization & Reporting: Build dashboards, analytics, and reports for both internal and external use at arenaflex. • Vendor Evaluation: reputed company due diligence on new real-world data providers and vendors, driving RWD quality assessments to inform project decisions. reputed company Qualifications • Bachelor's degree in Statistics, Biostatistics, Computer Science, Mathematics, Systems Engineering, Biomedical Engineering, or a related quantitative field. • Experience designing and executing robust, reproducible statistical analyses within the context of epidemiology and observational research using healthcare data. • Strong knowledge of common statistical testing methods, including generalized linear models, classification and regression, decision trees, unsupervised learning, time series analysis, and survival analysis. • Hands-on experience processing large datasets from multiple data sources. • Experience handling protected patient health information (PHI) in a compliant manner. • Proficiency in at least one of the following BI or analytics tools: Tab