Data Scientist (Machine Learning & AI)
Cyntegrity · Germany
IT Services and IT Consulting · 51-200 employees
About the role
Design, develop, and validate statistical, machine learning, and AI models to analyze complex clinical trial data. Collaborate with cross-functional teams to transform business challenges into production-ready analytical solutions and communicate findings to stakeholders.
What they look for
Requirements
Requires a bachelor's degree in a quantitative field and 3+ years of professional experience with Python or R for machine learning and statistical analysis. Experience with clinical research or healthcare data and familiarity with software engineering best practices are highly valued.
Benefits
Full description
Join Cyntegrity, one of the leading providers of clinical trial risk management technology. We're proud of our innovative MyRBQM® Portal and MyRBQM® Academy solutions that help pharmaceutical companies improve the quality, safety, and efficiency of clinical research.
We're looking for a Data Scientist (Machine Learning & AI) to join our growing Clinical Monitoring & Analytics team. If you enjoy solving complex problems with statistics, machine learning, and AI, and want your work to directly improve clinical trials and patient outcomes, we'd love to hear from you.
What You'll Do
As a Data Scientist at Cyntegrity, you will:
- Design, develop, and validate statistical, machine learning, and AI models.
- Build and evaluate Proofs of Concept (PoCs) for new analytical solutions.
- Analyze complex clinical trial data to identify patterns, risks, and insights.
- Collaborate with Product, Engineering, Clinical Monitoring, and customer-facing teams to transform business challenges into analytical solutions.
- Develop production-ready analytical components together with software engineers.
- Present findings through clear visualizations and communicate results to both technical and non-technical stakeholders.
- Continuously explore new technologies and contribute innovative ideas to our products.
What We're Looking For
Education
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Physics, or another quantitative field.
Experience
- 3+ years of professional experience using Python and/or R for statistical analysis and machine learning.
- Hands-on experience building, validating, and improving predictive models.
- Experience designing and executing analytical or machine learning Proofs of Concept.
- Experience with clinical research or healthcare data is a strong advantage.
Skills & Knowledge
- Strong understanding of statistics, hypothesis testing, regression, feature engineering, and model validation.
- Practical experience with machine learning and AI techniques.
- Excellent analytical thinking and problem-solving skills.
- Ability to communicate complex ideas clearly to different audiences.
- Familiarity with software engineering best practices, version control, testing, and reproducible analytical workflows.
- Understanding of GCP, clinical trial processes, or risk-based quality management is a plus.
Nice to have
- Experience with cloud-based analytics environments (Azure, AWS, Databricks, Snowflake).
- Experience with modern ML frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
What We Offer
- The opportunity to build AI solutions that help improve clinical research worldwide.
- Fully remote work with flexible working hours.
- An international, collaborative, and supportive team.
- High level of autonomy and ownership.
- Professional growth and continuous learning opportunities.
- A purpose-driven environment where your work has real impact.
About Cyntegrity
Cyntegrity develops cloud-based software that helps sponsors and CROs manage risk throughout the clinical trial lifecycle. Our mission is to make clinical research smarter, safer, and more efficient through data-driven technology and innovation.
Learn more about us at www.cyntegrity.com.
If you're passionate about applying data science and AI to meaningful real-world challenges, we'd love to hear from you.
