Job Description

About the role:

We are seeking a highly motivated Associate Director, Quality Engineer/Data Science to work in this exciting new area of allogeneic cell therapy. In this role, you will report to the head of Validation and Quality Engineering, and you will be responsible for developing and leading the Quality Data Science/Quality Engineering function to embed the principles and practices of applied statistics, data science and machine learning to further Allogene's efforts in leading the establishment of an allogeneic CAR T platform, and to provide guidance and data driven insights across Allogene's programs. You will work closely with respective project teams to establish a quality data science roadmap to implement a lifecycle approach for product quality control strategies, including tools and technologies to drive innovation and continuous improvement. The position is based out of our headquarters in South San Francisco, CA.

Responsibilities include, but are not limited to:

• Develop and execute strategy and roadmap to establish quality data science as a core capability for building allogeneic platform knowledge and establish data analytics to enable predict and prevent capabilities across GMP operations.
• Provide direction and input to project teams utilizing the principles of applied science and statistics to drive strategies for process and analytical understanding and overall product quality control, including approaches for product comparability, process validation, product monitoring, stability trending and lifecycle management/improvement.
• Contribute to the build out of the Allogene GMP Quality Management System by developing procedures and providing significant input and support to Product Quality Review and Management Review tools and practices.
• Serve as company authority on quality data science and QE activities in support of regulatory submissions and inspections, including assuring inspection readiness.
• Support root cause investigations and ensure data-driven decision making.
• Champion the use of data analytics and operational excellence tools to drive process improvement and innovation across the Allogene enterprise.
• Integral member of cross-functional teams to garner interdisciplinary insights (e.g. technical, clinical) to further accelerate Allogene's leading approach to the establishment of an "off-the-shelf" allogeneic CAR T platform.
• Collaborate across functions and stakeholders to develop a Knowledge Management approach for Allogene.
• Routinely scan external environment and engage in industry forums to proactively identify and implement best practices and new technologies.

Position Requirements & Experience:

• BS/MS degree in Statistics, Data Science, or related field (advanced degree preferred), ASQ Quality Engineer certification preferred, with at least 6-8 years of relevant experience in the biotechnology or pharmaceutical industry.
• Strong expertise in applied statistics (e.g., DoE, multivariate analysis, regression, time-series).
• Hands-on experience with tools such as R, Python, JMP, SAS, or equivalent.
• Experience working with complex data from laboratory, process, or manufacturing systems and ability to translate complex data insights into actionable business recommendations.
• Familiarity with visualization tools.
• Comprehensive knowledge of GMP regulations, guidance, and industry best practices.
• Proven success influencing cross-functional teams and senior stakeholders without direct authority.
• Comfortable in a fast-paced small company environment and able to adjust workload based upon changing priorities.
• Candidates must be authorized to work in the U.S.

We offer a chance to work with talented people in a collaborative environment and provide a top-notch compensation and benefits package, which includes an annual performance bonus, equity, health insurance, generous time off (including 2 annual holiday company-wide shutdowns) and much more. The expected salary range for this role is $170,000 to $210,00 per year. Actual pay will be determined based on experience, qualifications, geographic location, business needs, and other job-related factors permitted by law.

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