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’. There are so many reasons to join us. 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. The primary responsibilities of this role, Statistical Scientist, are to: As part of the Smart Operations Data Science team, this role will help shape the digital strategy for Bayer trial analytics new innovation model; Support product development through hypothesis generation, experimental design and statistical analysis & modeling for lab, controlled environment and field-based experiments for all business units teams in St Louis, Monheim and across the globe; Partnering with colleagues & data science teams across Bayer Crop Science is critical for success; Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline; Independently perform statistical analysis, computer programming, predictive modeling and experimental design; Use advanced statistical techniques and tools, and strong business acumen to deliver insight, recommendations, and solutions; Build cross-functional relationships to collaboratively partner with the business and effectively network within the Statistics Community; Identify limitations and improves upon existing methodologies and find novel applications of existing methodologies, and use a consultative approach while recognizing own technical limitations; Clearly explain technical and non-technical aspects of their work with other statisticians and stakeholders, both orally and in writing; Actively listen and recognize when the expertise of others should inform decisions; Partner effectively with subject-matter experts to deliver relevant solutions to well-scoped problems; Brings critical eye to own work; Ask good clarifying questions and quickly moves ahead; Adapt to changing priorities by switching between analysis projects; Approach adoption of new statistical methods with an open mind; Effectively sense and attend to changing needs of the consumer of the analyses while maintaining scientific integrity; Understand and can explain principles of statistics and machine learning, and can use modern software languages to apply their knowledge to provide solutions; Understand questions being asked in the given domain and applies integrated consultancy skillset to collaborate with subject matter experts and key stakeholders; Understand design principles and uses that understanding to clarify discussion and efforts around the proper uses, analyses, and interpretations of data, especially data from designed experiments; Understand complex statistical analysis methodologies and can implement such methodologies to solve complex problems; Use basic principles of experimental design and samples size to ensure correctness of trial design during discussions on trial planning with the stakeholders; Ensure correct statistical programming and analysis consistent with the experimental objectives and decision playbooks; Understand policies on data science best practices and ensure adherence to those best practices by associate statistical scientists; Present analysis results to business leaders and stakeholders ensuring accurate interpretation of the data and the analysis results.