Applied scientist Jobs in Eppertshausen

Join our team and take part in delivering cutting-edge statistical input and Real World Data (RWD) analyses essential for driving clinical development, regulatory compliance, and pharmacovigilance initiatives. Your role will involve architecting, designing, and scaling data analytics solutions, predictive modeling, data science, mining, visualization, and implementing algorithms.You will be instrumental in writing statistical sections of study protocols, collaborating closely with study teams, and effectively communicating results to relevant stakeholders. Your expertise will also extend to articulating, refining, and defending data analytics standards while applying advanced analytics techniques. This may entail processing large datasets, scaling algorithms, or creating user-friendly application interfaces.In addition to meeting timelines and maintaining high-quality deliverables in line with company and international standards, you will provide invaluable RWD / RWE advice to both internal and external stakeholders.If you're passionate about RWD / RWE and novel methods to drive informed decision-making and thrive in a dynamic, collaborative environment, we invite you to join us in shaping the future of healthcare. Who you are :  - Background in health and life sciences (biostatistics, epidemiology), or quantitative data sciences - Doctoral and / or master’s degree (e.g., PhD, MSc) in Biostatistics, Epidemiology, Mathematics or related field- Extensive prior experience with RWD / RWE required - Prior experience in the pharmaceutical industry required (4-8 years)- Demonstrable experience in writing and implementing statistical analysis plans for studies required - Demonstrable experience in processing large real-world datasets for analyses and studies required - Experience in predictive modeling, machine learning and artifical intelligence methodology a plus - Experience in coding (r) a plus - Excellent oral and written communication skills and demonstrated ability to engage and communicate scientific evidence to peers - Collaborative, proactive working style, with ability to work independently.