Data Scientist - Computer Vision

Overview

Job Title

Data Scientist - Computer Vision

Job Description

Your role:

The Data Scientist is responsible for ensuring data quality, accuracy, and reliability through rigorous validation and cleansing techniques, driving comprehensive quality control measures, working under limited supervision. The role utilizes advanced algorithms and AI, participates in end-to-end data mining projects to extract deep insights from complex data sources. The role deploys and tests data science solutions rigorously, ensuring optimal performance and seamless integration with enterprise systems. The role maintains robust data pipelines and workflows and leverages big data technologies to support advanced analytics projects.

You're The Right Fit If

• 7-10 years experience in AI and Data Science field. Master's in computers, electronics, electrical engineering or PhD in relevant streams
• Ability to frame business problems as computer vision and data science tasks (e.g., defect detection, OCR, surveillance analytics, medical imaging diagnostics)
• Collect, clean, label, and curate large image/video datasets; design augmentation strategies and manage dataset versioning
• Develop, train, and optimize deep learning models for tasks like image classification, object detection, segmentation, tracking, and anomaly detection using frameworks such as PyTorch, OpenCV or TensorFlow
• Solid understanding of machine learning and deep learning, including CNNs, modern architectures (e.g., ResNet, EfficientNet, Unet, YOLO transformers for vision), and training best practices.
• Apply and combine traditional vision (e.g., filtering, feature extraction, geometric transforms) with deep models where appropriate.
• Evaluate models using appropriate metrics (e.g., mAP, IoU, precision/recall, F1) and run experiments/ablation studies to improve performance and robustness
• Experience with cloud platforms (AWS, GCP, Azure) and version control; familiarity with MLOps tools is often preferred
• Excellent understanding of machine learning techniques and algorithms like k-NN, SVM, Random Forest, privacy models, etc.
• Understanding exploratory data processing techniques such as cleaning and verifying the integrity of data used for analysis
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Strong analytical, problem solving and communication skills
  
  

We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.

About Philips

We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

• Learn more about our business.
• Discover our rich and exciting history.
• Learn more about our purpose.
  
  

If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.