Winter, Spring 2026 Co-op-Intern Data Science and Digital Transformation

About the Role

Key Responsibilities

• Develop, train, and validate predictive models to support decision-making processes for bioprocess control.
• Apply machine learning and deep learning techniques to large-scale bioprocess datasets, including time-series data and sensor outputs.
• Use multivariate data analysis (MVDA) techniques to analyze complex, high-dimensional datasets and extract key process insights.
• Clean, preprocess, and structure data from various sources (e.g., sensors, spectrometers, batch records).
• Assist in designing and implementing data pipelines for real-time data collection, storage, processing, and visualization on digital bioprocessing platforms.
• Collaborate with teams to integrate predictive models into bioprocess monitoring systems.
• Contribute to the development of digital twins and soft sensor frameworks that simulate bioprocess performance and provide real-time decision support.
• Develop models to predict process deviations, improve efficiency, and ensure high-quality outcomes in biologics production.
• Support automation efforts by integrating machine learning models into decision-making workflows that optimize process control and reduce variability.
• Analyze complex datasets to generate actionable insights into bioprocess performance and suggest improvements.
• Work with biologists, process engineers, and other stakeholders to interpret model outputs and translate them into experimental or process optimization recommendations.
• Develop and deploy predictive or classification models for monitoring critical process parameters (CPPs) and quality attributes (CQAs).
• Create prototypes of data integration pipelines that enable the seamless flow of real-time data into digital dashboards or simulation models.
• Contribute to scientific reports, presentations, and publications that document methodology, results, and insights gained through model development.
• Provide recommendations for data science strategies and tools that enhance biomanufacturing process control and enable smarter, faster decision-making.

Requirements

• Must be currently enrolled and pursuing a master's degree or PhD in Data Science, Computer Science, Bioinformatics, Chemical Engineering, Biostatistics, or related quantitative discipline at an accredited college or university with the expectation of completing the degree by Spring 2028.
• Must be able to relocate to the office location and work 40 hours/week, Monday-Friday, for the full duration of the co-op/internship.
• Experience with Python or R for data analysis and machine learning modeling.
• Experience with machine learning libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa.

Nice to Have

• Experience with Process Analytical Technology (PAT), digital twins, soft sensors, or other data-driven tools in bioprocessing.
• Exposure to omics data, process data integration, or other biotech-related datasets.
• Coursework or research experience in bioprocessing, process control, or biosystems engineering.
• Understanding of statistical analysis, data visualization, and basic ML model evaluation techniques.
• Familiarity with time-series data, dimensionality reduction (e.g., PCA, t-SNE), and large-scale data processing methods.
• Experience with SQL and data pipeline tools (e.g., Apache Spark, Airflow).
• Understanding of biomanufacturing processes, particularly in cell culture, fermentation, or bioreactor operations.
• Strong communication and collaboration skills across technical and scientific teams.
• Ability to work independently and proactively in a fast-paced, research-driven environment.
• Eagerness to learn new concepts and technologies related to data science and bioprocessing.

Benefits & Perks

• Exposure to cutting-edge technologies and research methodologies.
• Networking opportunities within Sanofi and the broader biotech community.
• A supportive, future-focused team and a well-crafted rewards package.
• Opportunities for career growth and development, including promotions or lateral moves.