Data Scientist

The Data Scientist will be part of R&D Cell Therapy Media team, responsible for planning and execution of projects in co-operation with the rest of the team. The holder of this position will be responsible for the completion of product development projects with data science elements, integrating data and ML-based methods in cell culture technologies and for the accuracy of the results within the project timelines.

The Data Scientist will be part of R&D Cell Therapy Media team, responsible for planning and execution of projects in co-operation with the rest of the team. The holder of this position will be responsible for the completion of product development projects with data science elements, integrating data and ML-based methods in cell culture technologies and for the accuracy of the results within the project timelines.

Main Responsibilities & Tasks:

Methodology Development: Develop new data analysis methods and technologies in line with the department roadmap. This includes performing statistical design and evaluation of experiments, multivariate data analysis, and harnessing predictive modeling approaches.
ML Pipeline Management: Work on creating Machine Learning (ML) pipelines for the technology area, researching and implementing the right methodologies for different applications.
Continuous Improvement: Monitor model performance and make iterative improvements based on feedback and new data.
Innovation: Explore novel AI/ML approaches to enhance cell culture media development processes.
Project Leadership: Propose, execute and lead ML- and data-based work packages within defined projects in related technology areas according to company strategy and guidelines. This includes serving as a team member in cross-functional PMO projects.
IT & Data Infrastructure: Plan and drive implementation of required IT infrastructure for delivering project results. Support data collection from lab devices and other lab-related sources, ensuring seamless integration into the Sartorius data platform.
Reporting: Evaluate, analyze and present project results in internal and external meetings.
Expert Engagement: Actively participate in internal and external expert groups and/or networks in his/her technology area to bring in innovation and remain competitive.
IP Monitoring: Responsible for staying informed on and evaluating the IP situation for state-of-the-art technologies.
Data Pipeline Maintenance: Design, build, test and maintain robust data processing pipelines. Collaborate closely with other data science stakeholders to design and maintain data models for analysis and reporting.
Stakeholder Collaboration: Engage actively with local lab scientists as well as the Media Analytics team to better understand lab data, facilitating effective pipeline creation and optimization. Collaborate closely with the Data Science and Innovation team on data preprocessing and preparation.
Team Culture: Exemplify and foster a culture of continuous learning and innovation within the team.
Qualification & Skills:

Education

Master’s degree in Science, Engineering or Computer/Data Science; minimum Bachelor's degree in a relevant field.
Basic understanding of SQL and database concepts.

Work Experience

Experience in industry with a proven track record (experience in respective technology areas of Sartorius is a plus).

Methodological Competence

Very good written and verbal English communication skills.
Ability to work self-driven and with a high level of self-motivation.
Strong analytical and conceptual thinking skills to solve complex problems.
Willingness to learn new data analytical technologies and curiosity about new fields like cell biology.
Interest in cloud data platforms and data engineering.
Confident handling of MS Office.
3+ years of work experience (or 7+ years of work experience with a Bachelor's degree).
Solid working knowledge of Python is required, with familiarity in libraries such as Pandas, scikit-learn and other Python ML libraries.
Experienced in statistical analysis and relevant programming languages and software tools.
Deployment capabilities and adaptation of existing data-driven solutions to new applications.