Data Analyst Associate

About the role

The Associate Data Analyst will support the development, execution, and digital integration of Process Performance and Product Quality Monitoring (PPPQM) capabilities. This role sits at the intersection of bioprocessing, data analytics, and digital technology, using manufacturing and process data to enable reliable process monitoring, improving data quality, automate engineering workflows, and providing actionable insights to scientists and engineers. The ideal candidate combines a strong quantitative or scientific foundation with curiosity about data and technology. Candidates with experience in process development, manufacturing, attribute sciences, QC, or other biopharmaceutical functions who are interested in transitioning into a more digital and data-focused role are strongly encouraged.

Responsibilities

• Execute Process Performance and Product Quality Monitoring activities, including Annual Product Review (APR) analytics, process monitoring documentation, control-limit calculations, and other recurring monitoring deliverables.
• Support the migration and digitalization of monitoring strategies and associated documentation from legacy software to updated new software solutions.
• Ensure manufacturing and process-monitoring data are accurate, complete, and readily available by performing data-quality checks, troubleshooting pipelines, and integrating new data sources.
• Analyze and organize large, complex data sets using tools such as SQL, Python/R, Spotfire, Tableau, SIMCA, PI, and related data platforms.
• Develop, enhance, automate, test, and maintain dashboards, analytical models, data products, and digital workflows used by process development and manufacturing teams.
• Troubleshoot existing analytical and digital solutions and perform testing to ensure data products are robust, accurate, and fit for their intended use.
• Explore practical applications of AI, advanced analytics, modeling, and automation to simplify scientific workflows and improve how teams identify and respond to process signals.
• Partner cross-functionally with scientists, engineers, data scientists, and other stakeholders to translate business and scientific needs into practical data solutions and execute projects from concept through delivery.
• Composing Process Performance and Product Quality Monitoring deliverables. Examples include collating data for Annual Product Reviews, ensuring Process Monitoring documentation is in place, calculating control limits for our process monitoring program, completing APR deliverables accurately and on time, and ensuring control limit deliverables are completed timely and accurately.
• Ensuring data quality for Process Monitoring by confirming data is readily available in proper pipelines on a timely and accurate basis. This may include troubleshooting pipelines, adding conditions to process monitoring databases, or adding new sources to databases.
• Assisting with migration and data quality checks as monitoring strategy documentation moves from the current repository to the new software solution.
• Troubleshooting existing dashboards and data visualization products owned by the team including but not limited to Spotfire Reports, Smartsheet Dashboards, Knowledge Management Databases, SIMCA multivariate models, etc.
• Building existing capabilities into existing tools or build new digital tools through the use of applications such as SIMCA, Spotfire, Tableau, or custom applications as required by internal customers.
• Working with large quantities of data to enable general workflows such as data processing, cleaning, and labeling.
• Troubleshooting and testing data solutions to ensure desired data structures are robust and complete.
• Collaborating with multiple stakeholders to understand project timelines, create a project plan, and execute the project deliverables in a timely fashion.
• Developing, enhancing, automating, and managing analytics models.

Qualifications

• Previous experience working in the pharmaceutical, bioprocessing, or biotechnology industry
• Bachelor’s degree in engineering, computer science, mathematics, statistics, life sciences, or another quantitative discipline. Biopharmaceutical experience is preferred, particularly within process development, clinical or commercial manufacturing, attribute sciences, or QC.
• Candidates should have familiarity with one or more data or analytical technologies such as SQL, Python, R, Databricks, Spotfire/Tableau, SIMCA, Aveva PI, DeltaV, or Dassault Systèmes, Codex or Claude tools.
• Experience with statistical analysis, data visualization, data engineering, application development, or AI-assisted development tools is beneficial.
• Successful candidates will demonstrate strong problem-solving skills, attention to detail, intellectual curiosity, and the ability to learn new scientific and technical concepts quickly. They should be comfortable working across functions, managing multiple priorities, breaking ambiguous problems into actionable tasks, independently developing solutions, and incorporating feedback to continuously improve their work.

Skills

• Previous experience working in the pharmaceutical, bioprocessing, or biotechnology industry and demonstrated