Data Scientist – Computer Vision

We are looking for an experienced Data Scientist with strong hands-on expertise in Computer Vision and Applied Machine Learning to develop, fine-tune, evaluate, and optimise vision-based AI models. The ideal candidate will have practical experience working with modern object detection, tracking, and segmentation architectures, using frameworks such as PyTorch, TensorFlow, and OpenCV.You will work with image and video datasets, experiment with state-of-the-art computer vision models, and optimise model performance for real-world deployment requirements.Key ResponsibilitiesDevelop, train, and fine-tune computer vision models for object detection, object tracking, and image segmentation using PyTorch, TensorFlow, and OpenCV.Work hands-on with modern architectures, including YOLO, RF-DETR, and Vision-Language Models (VLMs), adapting and fine-tuning models for specific use cases.Curate, preprocess, clean, and augment image and video datasets in collaboration with annotation teams.Train, evaluate, and iteratively improve models against performance metrics, including mean Average Precision (mAP), precision, and recall.Track experiments, model versions, and performance results using tools such as MLflow or Weights & Biases.Optimise models for inference and deployment using technologies such as TensorRT and ONNX, taking real-world performance and deployment constraints into account.RequirementsBachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.3–7 years of hands-on applied Machine Learning experience, with a strong focus on Computer Vision.Strong Python programming skills and practical experience with PyTorch or TensorFlow, together with OpenCV.Demonstrated experience developing or fine-tuning models for object detection, tracking, and segmentation.Practical knowledge of computer vision architectures such as YOLO; experience with RF-DETR is relevant.Familiarity with model evaluation metrics, dataset preparation, data augmentation, and iterative model improvement.Experience with experiment-tracking tools such as MLflow or Weights & Biases.Familiarity with model optimisation and inference technologies such as ONNX and TensorRT.Preferred: Hands-on experience fine-tuning Vision-Language Models (VLMs).Important Application CriteriaCandidates must meet the relevant technical experience requirements and have practical Computer Vision experience.