Scientist, CMC Quantitative Sciences

In this role, you will partner with scientists, engineers, manufacturing, analytical, quality, regulatory, and digital teams to solve scientific and operational problems using fit-for-purpose quantitative approaches. You will independently lead complex analyses and defined workstreams supported by technical direction from the team. The successful candidate will bring strong technical depth in at least one quantitative discipline and an interest in building breadth across related capabilities. A background that combines applied statistics with machine learning or predictive modeling in the pharmaceutical industry is particularly valuable. This position is based onsite in Norwood, MA, where close collaboration with laboratory, development, manufacturing, and quality partners is central to the role.

Here's What You'll Do

• Independently plan and execute moderately complex quantitative analyses, models, or data solutions that support research, development, manufacturing, analytical, quality, and product lifecycle decisions.
• Translate scientific and business questions into clear analytical objectives; identify data requirements, select appropriate methods, assess assumptions and limitations, communicate conclusions, and recommended next steps.
• Apply quantitative methods suited to the problem, including statistics, machine learning, mechanistic modeling, data science, and data engineering.
• Lead defined projects or workstreams with manageable risk and resource requirements; establish plans, coordinate contributors, manage timelines, identify dependencies, and escalate technical or delivery risks early.
• Partner directly with cross-functional scientists, engineers, manufacturing, analytical, quality, regulatory, and digital teams to ensure studies and analyses are appropriately designed and results are translated into practical decisions.
• Develop reproducible and traceable analyses, models, code, visualizations, reports, and technical documentation aligned with team standards and phase-appropriate GxP expectations.
• Contribute quantitative content and supporting analyses for technical reports, regulatory submissions, responses to health authority questions, and other controlled documents, with senior leader review as appropriate.
• Build, improve, or apply reusable methods, scripts, templates, dashboards, pipelines, and analytical tools that increase the consistency, scalability, and efficiency of CMC decision-making.
• Evaluate data quality and analytical readiness; work with data owners and partners to resolve gaps, document assumptions, and improve data practices at the source.
• Communicate complex technical and scientific information clearly to technical and non-technical audiences, including difficult findings, uncertainty, tradeoffs, and limitations.
• Provide informal mentoring and technical guidance to less-experienced colleagues; contribute to peer review, knowledge sharing, training, and continuous improvement within CMC QS.
• Stay current with relevant quantitative methods, digital technologies, regulatory expectations, and industry practices; introduce new approaches when they provide clear scientific or business value.

Here's What You'll Bring to the Table

Minimum Qualifications

• PhD (0 - 2 yrs), MS (5 - 8 yrs), or BS (8 - 10 yrs) in Statistics, Biostatistics, Data Science, Applied Mathematics, Chemical Engineering, Biochemical Engineering, or a related STEM discipline.
• Demonstrated depth in at least one quantitative area, such as applied statistics, machine learning or predictive modeling, data science, mechanistic modeling, data engineering, or digital analytics solutions.
• Experience independently selecting and applying quantitative methods to moderately complex scientific, technical, or operational problems.
• Proficiency with computer languages (R, Python, SQL, etc.) and statistical software packages (JMP, Statistica, Minitab, etc.) to manipulate and analyze data.
• Experience developing clear, reproducible analyses and communicating methods, assumptions, limitations, results, and recommendations to cross-functional partners.
• Ability to lead defined assignments or small to mid-sized workstreams, manage competing priorities, and deliver high-quality work within agreed timelines.
• Understanding of regulatory guidance (e.g., ICH, FDA, EMA, WHO).

Preferred Qualifications

• Breadth across more than one CMC QS capability, such as statistics, modeling, data engineering, automation, visualization, or digital solution development.
• Experience developing, validating, or deploying machine learning, mechanistic, predictive, or multivariate models in a scientific or manufacturing setting.
• Familiarity with pharmaceutical or biotechnology development, analytical development, manufacturing, quality systems, or regulated laboratory environments.
• Working knowledge of phase-appropriate GxP principles, data integrity, risk-based decision-making, and regulatory expectations such as ICH, FDA, EMA, or other health authority guidance.
• Experience with manufacturing, laboratory, or quality data systems, such as MES, LIMS, OSI PI, SAP, DeltaV, Chromeleon, laboratory instruments, cloud data platforms, or internal applications.
• Experience contributing to technical reports, regulatory submissions, controlled documentation, or responses to health authority questions.
• Experience building reusable analytical code, automated reports, dashboards, applications, data pipelines, or governed digital solutions.
• Knowledge of phase-appropriate GMP principles and risk-based decision making.

Competencies

• Applies sound technical judgment while balancing scientific rigor, practical importance, risk, and business need.
• Works effectively across disciplines and functions; clarifies roles, decisions, dependencies, and next steps when expectations are ambiguous.
• Takes ownership of assigned outcomes and communicates status, risks, and decisions needed in a timely and constructive manner.
• Handles multiple priorities in a dynamic environment without compromising quality, traceability, or stakeholder alignment.
• Exchanges complex technical information effectively; can guide or influence others through evidence and clear reasoning.
• Curious, adaptable, and willing to learn new scientific domains, methods, technologies, and ways of working.
• Committed to reliable execution, continuous improvement, and collaborative problem-solving.
• Adept of managing multiple projects in a dynamic environment.
• Detail-oriented with a passion for science and data analysis.
• Self-motivated with effective communication skills.
• Keen to learn & adapt, with the ability to pivot in the face of new data.