• Guide the development of highly connected data systems that enable data‑driven, model‑based analytics to improve pipeline effectiveness and efficiency;
• Work with multifunctional teams to enable data connectivity across the editing pipeline, integrating information from lab, greenhouse, and field operations;
• Collaborate with partner teams across Crop Science (Gene Editing, IT Enterprise, Data and Engineering) to automate decision making and improve operational efficiency to accelerate development of gene‑edited products;
• Serve as a key communicator translating business data knowledge and operational workflows into clear technical implementation plans for data scientists, data engineers, and developers;
• Demonstrate autonomy in building relationships and networks within your unit and across functions, most often with members of the Crop Genome Editing team and closely aligned partner teams;
• Act as a consultant to leadership and colleagues on digital strategy and data‑driven operations through clear, organized, and influential communication;
• Actively build your own acumen in biology, genome design, and digital operations while sharing best practices and learnings with the broader Biology and Genome Design community.

WHO YOU ARE

Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• PhD in Computational Biology, Computer Science and Engineering, or another relevant scientific field with a minimum of 6 years of relevant experience, or MS with 10+ years of relevant experience;
• Demonstrated track record developing data systems and pipelines that enable efficient product delivery and operational modeling;
• Demonstrated experience working collaboratively in cross‑functional and cross‑cultural teams to achieve common goals;
• Demonstrated experience leading and influencing activities of cross‑functional teams without direct reporting relationships;
• Ability to lead and influence key stakeholders through challenges and opportunities and to facilitate solutions.

Preferred Qualifications:

• Experience building data pipelines as a ML DevOps Engineer or Data Engineer (or equivalent) with strong proficiency in open‑source tools, containerization, and orchestration tools, as well as experience with data versioning and model management tools;
• Experience with Operations Research in industries with time‑sensitive delivery and a laboratory component;
• Experience analyzing large biological datasets and developing analytical pipelines using Python, R, or similar software and languages, including experience with cloud development environments (for example, Google BigQuery, AWS, or similar);
• Experience working in cross‑functional settings spanning gene editing, IT, data science, and engineering in an R&D or product development environment.