Site Name: Munich - Office
Posted Date: Sep 4 2025
GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.
Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.
Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.
Join our team as a Tech Data analyst Customer Analytics (m/f/d) in Germany and help us turn data into meaningful insights. In this role, you will work with diverse teams to solve complex problems and improve decision-making. We value curiosity, collaboration, and a passion for using data to make a difference. This is a hybrid role, offering flexibility to work both remotely and on-site.

QUALIFICATIONS & SKILLS:

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.
• Experience with programming languages like Python, R, or SQL.
• Strong analytical and problem-solving skills.
• Ability to work collaboratively in a team environment.
• Fluent in English, both written and spoken.

PREFERRED QUALIFICATIONS & SKILLS:

• Master’s degree or PhD in a relevant field.
• Experience with machine learning techniques and tools.
• Familiarity with cloud platforms like AWS or Azure.
• Knowledge of data visualization tools such as Tableau or Power BI.
• Proficiency in German is a plus.

• Analyze large datasets to uncover trends and insights that drive business decisions.
• Develop predictive models and algorithms to solve real-world challenges.
• Collaborate with cross-functional teams to understand their data needs and deliver solutions.
• Communicate findings clearly to technical and non-technical audiences.
• Ensure data quality and integrity throughout the analysis process.
• Stay updated on the latest tools and techniques in data science.