Risk Management Center of Excellence Data Scientist

Benefit Risk Management Center of Excellence Data Scientist provides scientific and analytical solutions for Benefit Risk Management (BRM), supporting Global Safety Leaders (GSL), Center of Excellence (CoE) and Medical Device Safety (MDS) in the use of data-driven tools and data sources. The BRM CoE Data Scientist Leads the development of innovative solutions using novel technologies which facilitate the analyses required to provide answers to medical questions and meet regulatory requirements.

Has a deep knowledge of analytical/data science methods, and tools, which enables identification of business needs and the capability to choose and implement the right solutions. Delivers solutions which increase the efficiency of data science and data analytics in BRM, combining data from different sources, and facilitating the generation of new insights which support the ongoing benefit risk management of Bayer products.

Has a good understanding of data science, statistics, machine learning, and Artificial Intelligence (AI) including modern LLM systems, is able to evaluate AI use cases for BRM, assess feasibility, and incorporate these technologies into operational systems. Scope (global, regional or local): global.

• Lead Data Science and Analytics projects, working closely with colleagues in multiple BRM Therapeutic Groups and across the organization (Regulatory Affairs, Clinical Development, and IT).
• Serve as a catalyst and drive the development of BRM data science and analysis/retrieval strategies, and data visualization solutions that benefit the whole BRM organization.
• Drive standardization of processes and develop standardized best practice solutions for recurring data science and analysis tasks across BRM.
• Lead sub-teams working on specific Data Science and Analytics projects or ideas, coordinating with BRM team members, project managers, and peers across CMO and IT.
• Support PV and BRM transition to utilize novel technologies to advance analytical tools and data sources.
• Experiment with data science prototypes and develop into to operational, validated end to end solutions.
• Fully utilise business intelligence capabilities to incorporate new and innovative solutions into templates/workbooks (e.g., in Spotfire) to increase the efficiency of analytics in BRM.
• Identify and implement AI-driven solutions to automate pharmacovigilance workflows, enhance signal detection, and optimize benefit-risk assessments.
• Collaborate with cross-functional teams to integrate generative AI and large language models (LLMs) into BRM platforms.
• Design robust experiments and monitoring to ensure AI solutions are safe, reliable, and valuable.

• Present new ideas to peers and senior BRM leadership and other stakeholders to get buy-in and set up new projects with the IT platform.
• Present new solutions and act as advanced trainer to BRM on Data Science and analytical tool use, database content and query strategies.
• Coach the GSLs on Argus database content by having a deep knowledge of Argus data, process rules, and develop a new way of aligning PV product data with regulatory systems for easier maintenance.
• Combine data sources to automatically perform calculations relating to frequencies, exposures, and reporting rates.
• Generate the data including aggregate summary tabulations, descriptive statistics, trend analysis, outliers, and correlations, and use this data to automatically populate report templates.
• Ensure compliance with computer system validation procedures and create documents such as user requirements specifications, system specifications and user acceptance test scripts, to support implementation of new GxP systems and change requests.
• Data architect/expert for BRM analytical platforms e.g., DAVIS.
• Act as deputy to Data Science and Analytics Lead regarding process manager to PV tools and BRM representative in forums such as change committees, digital initiatives, and digital councils.
• Explore opportunities to enhance BRM capabilities using emerging technologies, including automation and intelligent data processing.
• Collaborate with internal stakeholders to assess the feasibility of applying…