Biostatistician Lead

VitelyBio; Ahmedabad District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

VitelyBio in Ahmedabad, Gujarat, seeks an In Vitro Statistician with 4+ years of experience to design experiments, analyze data, and provide statistical insights for pharma/biotech R&D. You will use SAS, R, JMP or Python, ensure regulatory compliance (FDA/ICH/GLP), develop SAPs, and support submissions while collaborating with scientists.

Strong communication and problem-solving skills are essential to translate complex results into actionable decisions.

Qualifications

• 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 in pharma/biotech or life sciences.
• Strong knowledge of experimental design, regression models, ANOVA, DOE, and multivariate analysis.
• Proficiency in 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.

Responsibilities

• Design, develop, and analyze in vitro study experiments with statistical rigor.
• Apply appropriate statistical models for data interpretation.
• Collaborate with scientists to provide guidance in study design and result evaluation.
• Utilize SAS, R, JMP, or Python to conduct data analysis, visualization, and reporting.
• Validate data integrity and ensure regulatory compliance.
• Develop and review SAPs, study protocols, and technical reports.
• Support regulatory submissions with statistical documentation and reports.
• Stay updated with the latest statistical methods and best practices.

Skills

Statistical analysis

Experimental design

Data visualization

Regulatory compliance

Communication skills

Education

Master's or Ph.D. in Statistics/Biostatistics/Bioinformatics

Tools

SAS

R

JMP

Python

Job description

We are seeking a skilled In Vitro Statistician with 4+ 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 and 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.