Machine Learning ScientistBayer CropScience Limited • Tulsa, Oklahoma, United States

This job offer is no longer available

Machine Learning Scientist

Bayer CropScience Limited

Tulsa, Oklahoma, United States

Tulsa, Oklahoma, United States

About

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. Machine Learning Scientist At Bayer Crop Science, you have the opportunity to transform how we steward innovation in plant breeding through the power of machine learning; As a Machine Learning Scientist focused on Quality, Stewardship, and Compliance (QSC), you lead the development and execution of a strategic framework that enhances quality, compliance, and stewardship across Bayer Plant Breeding; In this role, you drive high‑impact initiatives in close partnership with Global Stewardship representatives, ensuring alignment with organizational standards and regulatory expectations; You collaborate extensively across functions and with ML, IT, and data stewardship teams to design and refine digital systems and analytical methods that streamline stewardship program management for Bayer Plant Breeding; You develop innovative, data‑driven solutions and visualizations that strengthen program efficiency and promote consistent adoption of internal policies and external regulatory guidance. YOUR TASKS AND RESPONSIBILITIES The primary responsibilities of this role, Machine Learning Scientist, are to: Report to the Quality, Stewardship, and Compliance Lead; Design and implement scalable tools and services for machine learning training and inference while selecting suitable cloud and on‑premises infrastructure for production‑level machine learning models; Play a hands‑on role in building interpretable ML products while incorporating suggestions from plant breeders, laboratory experts, and stewardship experts; Support and develop data visualizations to streamline and improve efficiency of Bayer’s Plant Breeding pipeline; Query Bayer Crop Science data to provide support and insight for incident management and process improvements; Leverage ML techniques to optimize and automate current QSC processes; Proactively leverage data and build insights to identify opportunities within QSC workflows that improve delivery (cost, timeline, etc.) for Bayer’s Plant Breeding; Implement data quality checks, security measures, and governance standards to ensure accuracy and reliability. An ideal applicant is internally driven and motivated by urgency as well as the potential of modernizing the stewardship, quality, and compliance area for Plant Breeding; brings hands‑on experience with productionizing machine learning models; and is willing to speak out and provide insightful suggestions. WHO YOU ARE Bayer seeks an incumbent who possesses the following: Required Qualifications: Bachelor’s degree in a relevant field with strong grounding in Machine Learning, with an emphasis on plant breeding preferred; Demonstrated experience conducting research and contributing to research projects, technologies, publications, intellectual property, or trade secrets; Demonstrated experience with open‑source deep learning frameworks such as TensorFlow and/or PyTorch, and strong competency in Python as a programming language; Demonstrated experience with large language models (LLMs) and retrieval‑augmented generation (RAG) approaches; Demonstrated experience with SQL and data visualization tools such as Spotfire and Power BI; Direct knowledge regarding the implementation of deep learning techniques, including Transformers; Experience with backend and API development; Experience and proficiency working with high‑dimensional data. Preferred Qualifications: Ph.D. with a minimum of 3 years of relevant experience; M.S. with a minimum of 5 years of relevant experience; B.S. with a minimum of 7 years of relevant experience. Employees can expect to be paid a salary between $96,800-$145,00Additional compensation may include a bonus or incentive compensation (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.. This salary range is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors. This posting will be available for application until at least May 20, 2026. YOUR APPLICATION Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer. To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below. Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders. Bayer is an E-Verify Employer. Location: United States : Residence Based : Residence Based Division: Crop Science Reference Code: 869411 Contact Us Email: hrop_usa@bayer.com #J-18808-Ljbffr

Tulsa, Oklahoma, United States

Languages

English

Notice for Users

This job was posted by one of our partners. You can view the original job source here.

Find similar jobs