We deliver 4.3 billion healthcare solutions to people every year, thanks to the flawless planning and meticulous eye for detail of our Manufacturing & Supply teams. With your talent and ambition, we can do even more to protect people from infectious diseases and bring hope to patients and their families.

Join a cross-functional innovation team at the forefront of digital transformation in biomanufacturing. We specialize in integrating advanced data science with cutting-edge analytical technologies to revolutionize bioprocess monitoring and control. Our mission is to enable smarter, faster, and more predictive bioprocessing workflows across upstream and downstream operations. In this role, you will be contributing to an exciting research project that aims to build innovative systems to optimize the biomanufacturing process and ultimately bring high-quality biologics to market faster and more efficiently.

We are seeking a highly motivated and curious Data Science Co-op to contribute to a research project focused on developing and deploying advanced machine learning (ML), deep learning (DL), and data engineering techniques to enhance digital bioprocessing systems. The primary goal is to design data-driven models that can be integrated into an advanced digital infrastructure, enabling real-time monitoring, predictive analytics, and automated decision-making in the production of recombinant proteins, vaccines, and other biologics.

In this position, you will work closely with experts in Process Analytical Technology (PAT), data science, and bioprocessing to help build scalable, intelligent systems that improve the predictability, consistency, and quality of biological manufacturing processes.

We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people’s lives. We’re also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?

Main Responsibilities:

The Role of Data Science in Bioprocessing

Machine Learning (ML): Predictive models that help monitor and forecast critical process parameters (CPPs) and quality attributes (CQAs), driving continuous improvement in biomanufacturing. These models allow for early detection of potential process deviations and ensure that desired product qualities are maintained.
Deep Learning (DL): DL algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can capture non-linear relationships in complex bioprocess data, such as time-series or spectral information, that cannot be identified with traditional methods. These insights lead to more accurate predictions and optimization of processes.
Image Processing & Computer Vision: This technology is used for the analysis of visual data from bioreactors, cell cultures, and other bio-manufacturing instruments. It can assist with detecting anomalies, identifying patterns in cell growth, and ensuring the stability of cultures, all of which help in process optimization.
Digital Twins & Soft Sensors: These tools simulate physical bioprocesses in real-time and predict their behavior using sensor data. By employing digital twins, you can test, monitor, and optimize bioprocesses virtually, improving decision-making capabilities and process design.
Process Analytical Technology (PAT): Data science is a critical enabler of PAT, which is an industry framework aimed at ensuring quality throughout the manufacturing process. By leveraging advanced analytics and real-time monitoring, PAT can be used to develop proactive strategies that detect and control process variations, leading to improved process consistency and product quality.

Model Development & Analysis

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.

Data Engineering & Integration

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.

Digital Bioprocessing Innovation

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.

Scientific Insight Generation

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.

Expected Outcomes

Model Deployment: Develop and deploy predictive or classification models for monitoring critical process parameters (CPPs) and quality attributes (CQAs).
Prototypes: Create prototypes of data integration pipelines that enable the seamless flow of real-time data into digital dashboards or simulation models.
Documentation & Reports: Contribute to scientific reports, presentations, and publications that document methodology, results, and insights gained through model development.
Recommendations: Provide recommendations for data science strategies and tools that enhance biomanufacturing process control and enable smarter, faster decision-making.

About You

Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or STEM OPT usually require future sponsorship for long term employment and do not meet the requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship

Basic Qualifications:

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 that you will complete your current degree by the Spring of 2028
Must be able to relocate to the office location and work 40hrs/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).

Preferred Qualifications:

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 is a plus.
Experience with SQL and data pipeline tools (e.g., Apache Spark, Airflow) is a plus.
Understanding of biomanufacturing processes, particularly in cell culture, fermentation, or bioreactor operations.
Ability to apply data science techniques to real-world challenges in a bioprocessing environment.
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.

Why Choose Us:

Bring the miracles of science to life alongside a supportive, future-focused team.
Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact.
Exposure to cutting-edge technologies and research methodologies.
Networking opportunities within Sanofi and the broader biotech community.

Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.

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Pursue progress, discover extraordinary

Better is out there. Better medications, better outcomes, better science. But progress doesn’t happen without people – people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let’s be those people.

At Sanofi, we provide equal opportunities to all regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, ability or gender identity.

Watch our ALL IN video and check out our Diversity Equity and Inclusion actions at sanofi.com!

US and Puerto Rico Residents Only

Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally inclusive and diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; natural or protective hairstyles; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.

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