Intern - Data Science & BioStatistics- Manufacturing

Job Description

We are looking for a motivated student who wants to experience what it is like to be part of one of the largest multinational companies in the pharmaceutical industry. We offer an internship opportunity within our Data Science & BioStatistics department with freedom and space for your own initiative.

Welcome to our team

The S&T Data Science & Biostatistics team plays a critical role in enabling data-informed decision-making across Global Animal Health Manufacturing. This role offers the opportunity to apply advanced statistical methods to complex challenges in pharmaceutical manufacturing, where data science applications differ from standard use cases: limited data availability, complex process mapping, and black-box bioprocesses that require robust statistical design and simulation. You will contribute to key business areas such as quality control, yield optimization, maintenance, deviation management, and regulatory submissions. As part of an innovation-driven and globally dispersed team, this position provides an excellent platform to expand your expertise in both advanced data science and applied statistics.

Your internship assignment

As an intern, you will have the following responsibilities:

• Support end-to-end data science and statistical projects to address manufacturing challenges, applying advanced methods (DOE, SPC, regression, GLMs, multivariate analysis, optimization, simulation, predictive modeling, machine learning, etc.).
• Perform data quality assessments and prepare reports for stakeholders and regulatory submissions.
• Develop reproducible, well-documented code and data science pipelines.
• Automate repeatable tasks and integrate data science into operational workflows.
• Stay current with modern predictive modeling, statistical methods, and machine learning methodologies.
• Provide a fresh perspective to the department by sharing your opinion.

The described tasks form the basic package and will often be performed independently after a proper training and learning period. These tasks may be supplemented with other projects and/or tasks on your own initiative or at the request of the organization.

What is required

• Student in Statistics, or related field with strong statistical emphasis.
• Knowledge of DOE, SPC, GLMs, multivariate methods, simulations, predictive modeling, time series, stochastic processes, optimization, and machine learning.
• Proficiency in R or Python.
• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal skills.
• Comfortable working independently as well as collaboratively in a global team.

What we offer

Interning at our company means working together with your team towards health in an international work environment. As an intern, you will have the opportunity to work closely with experts and learn from them. In addition, we offer:

• The chance to gain experience in the pharmaceutical industry
• An opportunity to showcase yourself for potential opportunities after your internship period
• A challenging and dynamic work environment with plenty of room for personal development and initiative
• Good guidance from an experienced specialist

Animal Health Division

Our Animal Health Division is a trusted global leader in animal healthcare. We are committed to preserving and improving the health, well-being, and performance of animals and the people who care for them. As a global team of professionals, we work together to make a positive difference in animal care and the world's food chain.

Required Skills

Accountability, Analytical Problem Solving, Biostatistics, Clinical Research, Communication, Data Analytics, Data Applications, Database Management, Data Entry, Data Quality Assessments, Data Science, Data Visualization, Document Management, Global Supply Chain, Global Team Collaboration, Health Economics, Maintenance Management, Microsoft Office, Office Applications, Pharmaceutical Manufacturing, Process Improvements, Process Mapping, Project Management, Reporting and Analysis