Senior System Manager

Process Manager for Data Sciences Services & Solutions

Hubble is a pioneering data hub that continuously integrates and enriches terabytes of external scientific textual data to support insight generation along the R&D value chain. It opens opportunities for statistical analyses, large-scale text-mining and other data science methodologies to tease out important details and detect trends within e.g., patents, scientific literature, and grants. The Hubble platform is available Bayer-wide, and offers API access for data scientists. This allows large scale, programmatic analysis as well as integration into processes and platforms. Hubble is a cornerstone of our digital transformation journey within R&D. It serves many users, systems and processes with key data & analyses by answering ~2 million queries per week. We continue to extend the platform to best support insight generation and scientific decision making with a precision medicine focus. For more information contact: Astrid Rheinländer.

Great meeting you!!! I am domain SME in Clinical and Healthcare systems.

Team R&D Master Data Management (MDM) is responsible for the processes and technologies used to ensure the consistency, accuracy, and completeness of critical data that is used across various departments and functions within the R&D organization. MDM plays a crucial role in ensuring the reliability of scientific and clinical data used for research, drug development, regulatory submissions, and decision-making. Key responsibilities include: define and implement data standards & policies, develop and maintain a centralized master data repository, establish data quality controls, ensure data steward- and ownership, manage data integration from different sources and systems, ensure compliance with regulatory requirements and provide training and support for end-users across R&D to ensure proper use and interpretation of data.

Scientific work in Pharma R&D requires a reliable high-quality body of data and knowledge. Bayer Pharma R&D has an extremely large amount of scientific data. The use of a Data Catalog significantly improves the findability of data and serves as a key pillar in the implementation of a comprehensive data strategy.

The Proprietary Information Management team, PIM, takes care of processes and systems related to either Compliance or Data Governance. Please talk to us if you need help with some cross-functional or collaboration-related topic or if you are interested to learn more about compound-related processes, compliance checks, lab notebook systems and similar things. For more information contact: Friederike Stoll.

Collaborations with external partners may lead to joint legal rights for compounds and data, which may restrict reusage in other projects. With the increasing number of collaborations at Bayer and with advancing time, memories of the project details fade, and reconstruction of the status will be nearly impossible. Therefore, we aim for a system to document the role of compounds, which will facilitate decisions on further usage in other collaborations. For more information contact: Miriam Wollenhaupt. Images taken from Microsoft Office free for use pictograms.

The SCLM4Future project together with the PRINCE (Preclinical Information Center) project as well as the CONDAS (Connecting Data for Science) team is an excellent example for a successful cross-functional collaboration between CD&O, RED preMed and DS&AI within Research & Development but also between Platform IT and external partners. Together the team achieved to overcome legacy manual processes, redundant maintenance work, and functional silos by introducing new digital solutions and concepts with a strong collaborative mindset. The newly released SCLM 2.0 (Standard Codelist Maintenance) & PTO (Preclinical Terminology and Ontology) systems will enable our organization to improve the quality of controlled terminologies with regards to standard codelists and codes while reducing maintenance time and effort. This will have a positive impact on the code/codelist consistency across clinical and preclinical, as well as the compliance to regulatory requirements (e.g. CDISC (Clinical Data Interchange Standard Consortium) Controlled Terminology). The approach to share our knowledge as well as our efforts across functions will be an important step to archive our goals to increase data quality, interoperability and reusability – or to make a long story short: to make our data even more FAIR. For more information contact: Daniela Bergann.