Intern Data Science - Future Talent Program
We couldn't confirm this posting on Abbott's site in our latest checks -- see current similar openings below.
About this opportunity
About Abbott
Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutrition, and branded generic medicines. Through our innovative solutions, we are committed to improving the health and well-being of people around the world.
The Position
For our Established Pharmaceuticals Business (EPD) based in Allschwil, Switzerland, we are seeking a highly motivated candidate for an Internship - Data Science. This 6-12 month internship offers a unique opportunity to contribute to Abbott's digital transformation journey and gain hands-on experience in advanced analytics, machine learning, and artificial intelligence within a global pharmaceutical organization.
The central Advanced Analytics team at EPD's headquarters in Basel helps define and lead the transformation toward becoming a global, data-driven organization through the use of advanced technologies including Machine Learning, Deep Learning, Generative AI, and Computer Vision.
The team works closely with the EPD Senior Leadership Team and plays a key role in driving sustainable business growth and operational excellence.
Reporting to the Senior Manager Data Science Solutions, you will support projects in the areas of Supply Chain and Optimized Manufacturing while working alongside experienced data scientists to develop innovative analytics solutions that generate measurable business value.
Key Responsibilities:
Collaborate with senior data scientists to understand business challenges and analytical requirements.
Support data collection, data cleaning, exploratory data analysis, and interpretation of results.
Generate insightful data visualizations and communicate findings to project stakeholders.
Assist in machine learning model development workflows, including feature engineering, model selection, and validation activities.
Document analytical approaches, findings, and processes to enable knowledge sharing and reproducibility.
Contribute to advanced analytics projects focused on Supply Chain and Manufacturing optimization.
Stay informed about emerging trends and technologies in data science, machine learning, and artificial intelligence.
Work collaboratively with business and technical stakeholders to deliver measurable business impact.
Required Qualifications:
Currently pursuing a Master's degree in Applied Mathematics, Computer Science, Engineering, Applied Statistics, or a related field.
Solid experience applying data science methodologies to solve business problems.
Knowledge of data mining, statistical modeling, predictive analytics, and machine learning techniques.
Programming experience in Python or R; knowledge of object-oriented programming languages such as Java, C++, or C is considered an advantage.
Understanding of machine learning deployment concepts and software engineering best practices.
Experience with testing frameworks and analytical libraries.
Knowledge of cloud-based data science environments such as AWS or SageMaker is a plus.
Strong analytical thinking, problem-solving skills, and ability to work in international environments.
Excellent communication skills and ability to present complex concepts clearly.
Fluent English language skills; additional languages are an advantage.
Why Join Abbott?
At Abbott, you can build a meaningful career while contributing to innovative healthcare solutions that improve lives around the world.
This internship offers:
Hands-on experience in advanced analytics, machine learning, and artificial intelligence.
Exposure to high-impact projects supporting global Supply Chain and Manufacturing functions.
Collaboration with experienced data scientists and business leaders.
An opportunity to contribute to Abbott's data-driven transformation strategy.
Development of advanced analytical, technical, and business skills in a global environment.
Exposure to cutting-edge technologies and real-world business applications.
A potential pathway toward future career opportunities within Abbott.
Interested in joining us? Apply today and become part of a team that is helping shape the future of healthcare.
Abbott is an equal opportunity employer committed to employee diversity and inclusion. We welcome applications from all qualified candidates.
Stay connected at www.abbott.com , Facebook www.facebook.com/Abbott and Twitter @AbbottNews and @AbbottGlobal .
#earlycareersDACH #earlycareers
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 33 comparable Intern/Fellow/Postdoc Biostatistics & Data Science roles across 13 biopharma companies.
Salary context
12 of 33 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 33 comparable roles
Seniority mix
33 of 33 peers have a known seniority level
Therapeutic area mix
3 of 33 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
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How we calculate "similar"
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Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.