Statistician

About the job

Overview We are seeking a highly motivated and detail-oriented Statistician (03 years experience) to join our MSAT Data Science MSAT team. This role is ideal for candidates with strong foundations in statistics, data analysis, and machine learning.The successful candidate will play a critical role in enabling data-driven decision-making, ensuring product quality, process robustness, and regulatory compliance through advanced statistical methodologies.

Key Responsibilities

• Exploratory Data Analysis & Statistical Analysis
• Perform EDA to identify trends, patterns, and root causes in manufacturing deviations and investigations.
• Perform statistical analysis on diverse datasets, including time-series data, point data and repeated measures data.
• Predictive & Stability Analysis
• Develop predictive models for process performance monitoring, anomaly detection, yield improvement, and risk mitigation.
• Conduct stability studies using linear and nonlinear regression models in compliance with regulatory guidelines (ICH).
• Subject Matter Expertise
• Act as a subject matter expert (SME) in statistics, statistical learning, and data science methodologies.
• Provide guidance to cross-functional teams on appropriate statistical approaches and best practices.
• Advanced Statistical Techniques
• Apply advanced methods including:
• Regression models
• Mixed-effects models
• Equivalence/non-inferiority testing
• Bayesian methods (desirable)
• Customize analyses based on project needs.

Required Qualifications & Skills

• Education
• Masters degree in Statistics, Biostatistics, Data Science, Mathematics, or a related field.
• Experience
• 03 years of relevant experience in statistical analysis, preferably in manufacturing, pharma, biotech, or CMC environments.
• Technical Skills
• Proficiency in at least one statistical/programming tool:
• R / Python (preferred)
• JMP, SIMCA, or equivalent tools
• Working knowledge of:
• Machine learning algorithms
• Multivariate data analysis (MVDA)
• Statistical modeling techniques
• Analytical Thinking
• Strong problem-solving skills with a data-driven and critical thinking approach.
• Ability to handle complex datasets and extract meaningful insights.
• Communication & Collaboration