At Bayer, we're seeking 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'll 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 organisation.
\nThe primary responsibilities of this role are to:
\nSolve real agricultural problems using deep learning and AI across image and other data modalities, translating complex models into tangible business and scientific impact.
\nDesign and implement end-to-end machine learning pipelines for computer vision use cases, including segmentation, classification, detection, and multi-task learning.
\nPrototype, evaluate, and iterate on cutting-edge architectures such as CNNs, Vision Transformers, foundational and large-scale vision models, ensuring state-of-the-art performance.
\nOptimize models for accuracy, robustness, and inference efficiency, including experimentation with hyperparameters, compression, and deployment-oriented optimisations.
\nIndependently build scalable data pipelines for training, validation, and evaluation, including data ingestion, augmentation strategies, and active learning loops.
\nCollaborate cross-functionally with product, data, and software engineering teams to integrate models into production systems and deliver reliable, maintainable solutions.
\nContribute to MLOps practices, including model versioning, deployment, monitoring, and retraining workflows using modern tooling and cloud-based platforms.
\nBuild 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.
\nPresent clear, compelling, and validated stories about experiments, results, and recommendations to peers, senior management, and internal customers to drive strategic and operational decisions.
\nWe seek an incumbent who possesses the following:
\nM.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.
\nProficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
\nHands-on experience with modern computer vision architectures including models such as ResNet, UNet, DeepLab, YOLO, SegFormer, SAM, and Vision Transformers.
\nStrong background in handling large-scale datasets and creating custom datasets, for example using frameworks such as Hugging Face Datasets.
\nSolid understanding of core machine learning concepts including loss functions, regularization, optimisation, and learning rate scheduling.
\nExperience developing and deploying models using cloud-based ML platforms such as AWS SageMaker.
\nFamiliarity with Unix environments, including bash, file systems, and core utilities.
\nStrong engineering practices including use of Git, Docker, CI/CD pipelines, modular codebase design, and unit testing.
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