Philips is seeking an experienced Data Scientist specializing in AI for Computer Vision to join our team. In this role, you will develop, fine-tune, and optimize AI models for computer vision applications in healthcare, collaborating with cross-functional teams to deliver advanced solutions.

What You'll Do

Explore and develop innovative Artificial Intelligence (AI) algorithms for healthcare applications.
Create and refine AI algorithms for pre- and post-processing of images and videos from various imaging modalities.
Develop and implement machine learning and deep learning techniques for segmentation, classification, and statistical modeling.
Design, train, and fine-tune deep learning models for real-world computer vision applications.
Gather, clean, and preprocess large-scale image and video datasets for training and evaluation.
Conduct A/B testing and assess model performance using quantitative metrics like IoU, mAP, precision, and recall.
Stay updated with the latest advancements in computer vision, deep learning, and related technologies.
Work with ML engineers to deploy models into production environments using cloud platforms like AWS and Azure and relevant frameworks.
Work closely with cross-functional teams to integrate computer vision solutions into business processes.

What We're Looking For

A Bachelor’s or master’s Degree in Computer Science, AI, Data Science, Machine Learning, or a related field.
3+ years of experience in machine learning, deep learning, or AI research.
At least 1 year of hands-on experience developing computer vision-based AI applications.
Strong proficiency in Python and ML frameworks like TensorFlow and PyTorch.
Strong problem-solving skills and the ability to work in a fast-paced, research-driven environment.

Nice to Have

Knowledge of computer vision, natural language processing (NLP), or multimodal AI applications.
Familiarity with computer vision techniques and fine-tuning of models.
Hands-on experience with MLOps tools like MLflow, Kubeflow, Docker, or Kubernetes.
Understanding of ethical AI and bias mitigation in computer vision models.
A strong publication record or contributions to open-source AI projects.