Senior Data Scientist -Manufacturing

Job Description

Job Description: Data Scientist with Pharma Background Role Overview: The Data Scientist will leverage expertise in pharmaceutical manufacturing data, particularly Electronic Batch Records (EBR), to develop predictive models, ensure data integrity, and drive analytics initiatives that enhance manufacturing compliance and efficiency. This role requires collaboration with cross-functional teams including manufacturing, quality assurance, regulatory, and IT to transform raw EBR data into actionable insights aligned with Good Manufacturing Practices (GMP) and Pharma 4.0 initiatives. Core Responsibilities Analyze and interpret large-scale EBR and manufacturing datasets to identify trends, anomalies, and opportunities for process improvement and risk mitigation. Develop and deploy advanced statistical models and machine learning algorithms to predict batch quality outcomes, detect deviations, and optimize production workflows. Ensure data integrity and compliance by working closely with quality and regulatory teams to maintain audit trails, electronic signatures, and documentation standards as per cGMP requirements. Integrate EBR data with other enterprise systems such as MES, LIMS, and ERP to create unified data models supporting end-to-end manufacturing analytics. Collaborate with IT and software developers to implement data pipelines, dashboards, and visualization tools that provide real-time insights to manufacturing and quality stakeholders. Lead efforts in digital transformation projects aligned with Pharma 4.0, including automation, real-time monitoring, and advanced analytics. Communicate complex analytical findings effectively to both technical and non-technical stakeholders to drive data-driven decision-making. Participate in validation and qualification activities for analytics solutions to ensure compliance with regulatory standards. Provide training and support to manufacturing teams to facilitate adoption of analytics tools and foster a data-driven culture. Required Skills and Experience Strong statistical and mathematical background with expertise in data mining, predictive modeling, and causal analysis. Hands-on experience with pharmaceutical manufacturing data, especially Electronic Batch Records (EBR), and understanding of batch production processes. Knowledge of regulatory compliance requirements related to data integrity, cGMP, and audit readiness in pharma manufacturing. Proficiency in programming languages such as Python and/or R for data analysis, modeling, and automation. Experience with cloud platforms (e.g., AWS) and big data technologies for scalable analytics solutions. Familiarity with integrating and analyzing structured and unstructured data from MES, LIMS, ERP, and EBR systems. Ability to manage complex data integration challenges and maintain secure, compliant data environments. Excellent problem-solving skills and ability to work collaboratively across geographies and functions. Prior experience in pharma or biotech industry analytics roles, preferably involving Medical Affairs or manufacturing data analytics. Knowledge of natural language processing (NLP) and advanced visual analytics is a plus. Preferred Qualifications 4+ years of advanced analytics experience, including machine learning and statistical modeling. 7+ years of experience with a strong focus on pharmaceutical data science. Experience in causal inference, AI/ML applications, and predictive modeling in pharma contexts. Familiarity with electronic signature systems, digital audit trails, and EBR software platforms. Strong communication skills to translate analytics into business insights and influence decision-making.