Data Scientist Computer Vision

Bayer•Chesterfield, MO

2d•$109,370 - $164,056

About The Position

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. Data Scientist Computer Vision Bayer is looking for a talented Data Scientist with deep learning and machine learning expertise focused on image-based data to help shape the future of agriculture; In this role, you join a dynamic team that supports the development of Bayer Crop Science next-generation products by applying computer vision to automate critical processes across the Plant Biotechnology organization; You work at the intersection of cutting-edge AI and real-world agricultural challenges, collaborating closely with scientists, engineers, and product teams to deliver scalable, production-ready solutions that directly impact innovation and operational excellence across the business.

Requirements

M.S. with 2+ years of experience or Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on machine learning or computer vision
Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow
Hands-on experience with modern computer vision architectures including models such as ResNet, UNet, DeepLab, YOLO, SegFormer, SAM, and Vision Transformers
Strong background in handling large-scale datasets and creating custom datasets, for example using frameworks such as Hugging Face Datasets
Solid understanding of core machine learning concepts including loss functions, regularization, optimization, and learning rate scheduling
Experience developing and deploying models using cloud-based ML platforms such as AWS SageMaker
Familiarity with Unix environments, including bash, file systems, and core utilities
Strong engineering practices including use of Git, Docker, CI/CD pipelines, modular codebase design, and unit testing.

Nice To Haves

Experience with major cloud platforms such as AWS, GCP, or Azure
Familiarity with MLOps tools such as DVC, MLflow, Kubeflow, or Airflow
Knowledge of generative models, multimodal architectures, vision-language models, or self-supervised learning techniques
Experience integrating generative AI frameworks such as OpenAI API, Anthropic, or LangChain into applications
Ability to design and maintain RESTful or GraphQL APIs to expose model inference services and data processing pipelines
Experience building interactive dashboards using tools such as Dash or React to visualize outputs and monitor model and system performance
Strong communication skills, including the ability to tell clear experiment stories, influence stakeholders, and provide mentorship within a technical team.

Responsibilities

Solve real agricultural problems using deep learning and AI across image and other data modalities, translating complex models into tangible business and scientific impact
Design and implement end-to-end machine learning pipelines for computer vision use cases, including segmentation, classification, detection, and multi-task learning
Prototype, evaluate, and iterate on cutting-edge architectures such as CNNs, Vision Transformers, foundational and large-scale vision models, ensuring state-of-the-art performance
Optimize models for accuracy, robustness, and inference efficiency, including experimentation with hyperparameters, compression, and deployment-oriented optimizations
Independently build scalable data pipelines for training, validation, and evaluation, including data ingestion, augmentation strategies, and active learning loops
Collaborate cross-functionally with product, data, and software engineering teams to integrate models into production systems and deliver reliable, maintainable solutions
Contribute to MLOps practices, including model versioning, deployment, monitoring, and retraining workflows using modern tooling and cloud-based platforms
Build strong cross-functional relationships and actively engage with the broader Data Science Community to share best practices, align on standards, and co-create innovative solutions
Present clear, compelling, and validated stories about experiments, results, and recommendations to peers, senior management, and internal customers to drive strategic and operational decisions.