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About the Role
The Senior Lead Data Scientist within RWDS&AI leads advanced analytics using real-world data, statistical methods, machine learning, and AI to generate evidence and insights for product commercialization, lifecycle management, and strategic decisions. The role combines technical expertise with scientific leadership, collaborating cross-functionally to design analytical solutions and accelerate evidence generation.
Responsibilities
- Lead and shape high-impact data science initiatives across the full analytics lifecycle, from study design and data feasibility assessments to advanced analyses, interpretation, and communication of insights using real-world data, statistical methods, machine learning, and AI.
- Design and implement innovative analytical solutions that advance real-world evidence generation, strengthen scientific rigor, and support product commercialization, lifecycle management, and healthcare decision-making.
- Build and scale AI-enabled analytical capabilities by developing vendor-agnostic AI agent toolkits, reusable machine learning components, and intelligent workflows that accelerate evidence generation.
- Drive the responsible adoption of AI by establishing practical validation approaches, governance principles, and quality standards that align with scientific, ethical, and regulatory expectations.
- Develop robust evaluation frameworks for AI-powered analytical workflows, ensuring transparency, traceability, reliability, and appropriate risk mitigation when working with complex real-world data.
- Act as a trusted scientific and technical leader, providing guidance, mentorship, and coaching to colleagues while fostering a culture of collaboration, continuous learning, and innovation.
- Partner closely with cross-functional teams to translate business and scientific questions into actionable evidence and insights.
- Contribute to the strategic growth of RWDS&AI by advancing data science methodologies, AI/ML capabilities, infrastructure priorities, and best practices, while representing the organization through scientific collaborations, publications, and conference presentations.
Requirements
- PhD (preferred) or MSc in Data Science, Computational Science, (Bio-)Informatics, Mathematics, Statistics, Physics, Engineering, Epidemiology, Econometrics, Machine Learning, or a related discipline.
- Extensive professional experience in Data Science, Advanced Analytics, or a related field.
- Experience within healthcare, life sciences, or the pharmaceutical industry is highly valued.
- Proven track record of leading complex analytical projects, influencing strategic decisions, and supporting the development of technical teams.
- Expertise in Real-World Data (RWD) and experience designing scalable analytical solutions, defining data models, assessing data feasibility, and selecting appropriate data sources to answer complex research questions.
- Deep expertise in Statistical Analysis, including causal inference, advanced adjustment methods, sensitivity analyses, and the development of scientifically robust analytical approaches aligned with regulatory expectations.
- Strong capabilities in Machine Learning, AI, and Software Engineering, including the development of reusable models, pipelines, AI-enabled workflows, and production-ready analytical solutions that create business value.
- Hands-on expertise in Data Science Infrastructure, Data Modeling, Ontologies, and Programming, with advanced proficiency in Python and/or R, SQL, modern analytical platforms, reproducible workflows, and standardized data models such as OMOP CDM.
- Collaborative communicator with strong analytical thinking, stakeholder management, problem-solving, adaptability, and innovation skills.
- Communicate fluently in English, both written and spoken.
Skills
- Data Science
- Advanced Analytics
- Real-World Data (RWD)
- Statistical Analysis
- Causal Inference
- Machine Learning
- AI
- Software Engineering
- Data Modeling
- Ontologies
- Python
- R
- SQL
- OMOP CDM
- Analytical Thinking
- Stakeholder Management
- Problem-Solving
- Adaptability
- Innovation
Location
- Germany
- Berlin
Work Type
- Hybrid work models
- Part-time arrangements
Experience Level
- Senior
- Extensive professional experience
Education Level
- PhD
- MSc
Salary/Compensations
- 109.800€ - 138.500€ per year (full-time)
- Variable component
- Global equity-based cash plan
Benefits
- Competitive salary
- Variable component
- Global equity-based cash plan
- Flexible work arrangements (hybrid, part-time)
- Company daycare centers
- Childcare support
- Time off for family care
- Summer camps for children
- Professional growth opportunities
- Learning and development programs
- Bayer Learning Academy
- Development dialogues
- Coaching and mentoring programs
- Health awareness programs
- Free health checks
- Inclusive work environment
About the Company
- At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility.
- We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’.
- If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.
- This is your opportunity to tackle the world’s biggest challenges with us: Maintaining our health, feeding growing populations and slowing the rate of climate change.
- You have a voice, ideas and perspectives and we want to hear them.
- Because our success begins with you.
- Be part of something big.
- Be Bayer.
Equal Opportunity
- Bayer welcomes applications from all individuals, regardless of race, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, pregnancy, sexual orientation, gender identity, gender expression or any unlawful criterion under applicable law.
- We are committed to treating all applicants fairly and avoiding discrimination.