Principal Statistician, CMC Quantitative Sciences

The Role: 

Moderna is seeking a strategic, hands-on Principal Scientist to join the CMC Quantitative Sciences (QS) team in Norwood, MA. As a key member of the team, you will lead statistical strategy and execution for analytical method lifecycle activities and applied modeling for analytical instrumentation across clinical, late-stage, and commercial programs. 

You will serve as the primary statistical thought partner to Analytical Development and Quality, guiding phase-appropriate strategies for method development, validation, transfer, and monitoring. In addition to traditional statistical techniques, you will apply quantitative and machine learning methods to analytical technologies and contribute to the implementation of next-generation QC tools, including automation and high-throughput analytics. You will also support regulatory submissions and help shape Moderna’s statistical approach to data monitoring and method comparability. 

Beyond your technical contributions, you will mentor others and help build a culture of statistical excellence across CMC. This is an ideal role for someone who thrives in applied problem-solving, enjoys coaching, and wants to make a tangible impact in a high-paced, science-first environment. 

The ideal candidate is self-driven, an excellent communicator, and aligned with Moderna’s core values of being Bold, Collaborative, Curious, and Relentless. Your leadership will help ensure our statistical approaches remain innovative, scalable, and essential to the success of our growing therapeutic platform. 

Here’s What You’ll Do:  

• Lead statistical support for analytical method lifecycle activities, including method development, validation, transfer, and investigations across clinical, late-stage, and commercial programs 

• Serve as a subject matter expert on statistical aspects of the analytical method lifecycle, guiding experimental design, data analysis, and decision-making 

• Contribute to regulatory filings by authoring and reviewing statistical content for reference standards, method validation, and method transfer in regulatory submissions 

• Drive analytical monitoring innovation by developing automated QC data monitoring systems and integrating industry best practices in analytical method lifecycle activities 

• Support next-generation analytics by enabling automated QC, high-throughput Raman, and advanced integration of complex chromatographic methods 

• Guide post-approval changes, including reference standard updates and analytical method optimizations 

• Partner cross-functionally with scientists, engineers, and quality teams to ensure experiments are well-designed and statistical methods are appropriately applied 

• Educate and influence partners by building statistical literacy and promoting the use of sound statistical reasoning in analytical decision-making 

• Communicate clearly to both technical and non-technical audiences, translating complex analyses into actionable insights 

• Champion statistical best practices to promote consistency, rigor, and scalability across CMC QS and the broader Moderna platform 

• Stay current and contribute to the field through literature review, training, and conference engagement 

Here’s What You’ll Bring to the Table: 

Minimum Qualifications 

• PhD in Statistics (5+ yrs), MS (8 - 10 yrs), or BS (10 - 15 yrs) in Statistics or a related field 

• Strong foundation in applied statistical methods such as DOE, regression, capability analysis, control charts, and multivariate analysis 

• Experience with analytical method validation and lifecycle activities in a regulated environment 

• Proficiency in JMP, R, or SAS 

• Effective communicator who can translate statistical thinking into actionable strategies for non-statistical audiences 

• Understanding of regulatory guidance (ICH Q2/Q14, USP, Ph.Eur., WHO) 

Preferred Qualifications 

• Familiarity with eCTD submissions and response to HA questions 

• Experience with analytical QC automation, high-throughput methods, or large-scale monitoring systems 

• Knowledge of phase-appropriate GMP principles and risk-based decision making 

• Proven mentoring or people management experience 

Competencies 

• Skilled at balancing practical importance, business impact, and scientific rigor associated with decisions 

• Strong collaborative skills in a cross-functional matrix organizational structure 

• Capable of managing multiple projects in a dynamic environment  

• Detail-oriented with a passion for science and data analysis 

• Self-motivated with outstanding communication skills 

• Keen to learn & adapt, with the ability to pivot in the face of new data