RWE Manager

Description : At Client , we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Position Summary The Real-World Evidence (RWE) team within Client is seeking a highly motivated Postdoctoral Scientist to join our Data Science & Digital Health (DSDH) Immunology group. This global role offers a unique opportunity to apply advanced analytics to real-world data (RWD) and contribute to the generation of evidence that informs clinical development, regulatory strategy, and healthcare decision-making across diverse populations and geographies. • Conduct high-impact research using large-scale observational databases and clinical trial data to generate real-world insights. • Design and execute studies in collaboration with global cross-functional teams, including clinical, regulatory, epidemiology, and data science. • Develop protocols, statistical analysis plans, and programming specifications aligned with international standards. • Deliver high-quality outputs including analysis-ready datasets, visualizations, and manuscripts for peer-reviewed publication. • Present findings to internal and external stakeholders, including global scientific and regulatory audiences. • Stay current with global trends in RWE methodologies, data privacy regulations, and health technology assessment. Required Qualifications • Masters in Epidemiology, Biostatistics, Data Science, Public Health, Biomedical Informatics, or a related quantitative discipline. Would prefer a PhD with qualifications. • Experience working with real-world data (e.g., electronic health records, claims, registries) from diverse healthcare systems. • Proficiency in R, Python, or SAS; familiarity with OMOP CDM and OHDSI tools is highly desirable. • Strong written and verbal communication skills in English; additional languages are a plus. • Demonstrated ability to work effectively in multicultural, multidisciplinary teams. Preferred Qualifications • Experience working with data sources from low- and middle-income countries (LMICs), including national health surveys, regional registries, or community-based studies. • Familiarity with LMIC health systems, including data infrastructure, care delivery models, and public health priorities. • Knowledge of global health policies, including WHO guidelines, international data governance frameworks, and health equity initiatives. • Experience with digital health technologies, such as mobile health platforms, remote monitoring tools, or AI-enabled diagnostics. • Prior collaboration with pharmaceutical companies, including experience in clinical development, regulatory strategy, or health economics and outcomes research (HEOR). • Experience with international regulatory submissions or health authority interactions. • Familiarity with machine learning, causal inference, and advanced statistical modeling. • Prior publications in peer-reviewed journals and presentations at global conferences.