In Vitro Statistician

Job Description

Job Summary We are seeking a skilled In Vitro Statistician with 2-7 years of experience to support our research and development teams in statistical analysis of in vitro studies. The ideal candidate will be responsible for designing experiments, analyzing data, and providing statistical insights to support decision-making in pharmaceutical, biotechnology, or life sciences research. Key Responsibilities: Design, develop, and analyze in vitro study experiments, ensuring statistical rigor and accuracy. Apply appropriate statistical models and methodologies for data interpretation. Work closely with scientists, biologists, and researchers to provide statistical guidance in study design and result evaluation. Utilize statistical software (e.g., SAS, R, JMP, or Python) to conduct data analysis, visualization, and reporting. Validate and verify data integrity, ensuring compliance with regulatory guidelines (FDA, ICH, GLP). Develop and review statistical analysis plans (SAPs), study protocols, and technical reports. Support regulatory submissions by preparing statistical documentation and reports. Stay updated with the latest statistical methods and best practices in in vitro research. Required Qualifications & Skills: Master’s or Ph.D. in Statistics, Biostatistics, Bioinformatics, or a related field. 2-7 years of experience in statistical analysis of in vitro studies, preferably in the pharmaceutical, biotechnology, or life sciences sector. Strong knowledge of experimental design, regression models, ANOVA, DOE, and multivariate analysis. Proficiency in statistical software (SAS, R, JMP, Python, or equivalent). Experience with data visualization, trend analysis, and predictive modeling. Understanding of regulatory requirements and compliance standards (FDA, ICH, GLP). Excellent communication skills to present findings to cross-functional teams. Strong problem-solving skills and attention to detail. Preferred Qualifications: Experience in bioassays, pharmacokinetics, biomarker analysis, or toxicology studies. Familiarity with machine learning and AI-based statistical modeling. Knowledge of clinical and preclinical statistical applications.