Data Scientist (Computer Vision & Machine Learning)

Job Responsibilities Develop and train computer vision models for tasks such as image classification, object detection, and segmentation. Work with image datasets, including data cleaning, annotation support, preprocessing, and augmentation. Apply deep learning frameworks to build and improve model performance. Implement and test approaches for anomaly detection, including unsupervised defect localization using PatchCore, and work with modern architectures such as Segment Anything (SAM) and Swin Transformers. Apply standard machine learning techniques such as Regression, Gradient Boosting, and Time-Series methods to integrate visual data with structured metadata for business insights. Evaluate model performance using metrics such as mAP, precision, recall, and F1-score, and follow established validation approaches to ensure model reliability. Assist in integrating models into existing pipelines and support deployment efforts. Work closely with cross-functional teams to communicate results to technical stakeholders and clients. Technical Skills & StackComputer Vision & Deep Learning (Core Focus): Libraries: PyTorch, OpenCV, scikit-image, Detectron2, MMSegmentation, and anomalib. Architectures: ResNet, Faster R-CNN, Swin Transformers, Autoencoders (SAE/VAE), and Masked Autoencoders (MAE). Concepts: Image classification, object detection, segmentation, basic CNN architectures Machine Learning & Data Science: Techniques: Supervised/Unsupervised learning, XGBoost, LightGBM, Random Forest, PCA/SVD (Dimensionality Reduction), and Cross-Validation. Libraries: Scikit-learn, XGBoost, Pandas, NumPy, SciPy, and Matplotlib. Data & Tools: Basic SQL knowledge Familiarity with Git or version control Experience & Education Experience: 4-5 years of overall experience, with recent handson work in Computer Vision projects for at least 1 year Education: Bachelors degree in Computer Science, Engineering, Mathematics, or related field Must-Have Requirements Handson experience with at least one deep learning framework (PyTorch or TensorFlow) Recent experience working on Computer Vision use cases Understanding of core computer vision concepts (CNNs, image preprocessing, model evaluation) Ability to write clean Python code for data processing and model development Strong willingness to learn and work in a fastpaced environment Job Responsibilities Develop and train computer vision models for tasks such as image classification, object detection, and segmentation. Work with image datasets, including data cleaning, annotation support, preprocessing, and augmentation. Apply deep learning frameworks to build and improve model performance. Implement and test approaches for anomaly detection, including unsupervised defect localization using PatchCore, and work with modern architectures such as Segment Anything (SAM) and Swin Transformers. Apply standard machine learning techniques such as Regression, Gradient Boosting, and Time-Series methods to integrate visual data with structured metadata for business insights. Evaluate model performance using metrics such as mAP, precision, recall, and F1-score, and follow established validation approaches to ensure model reliability. Assist in integrating models into existing pipelines and support deployment efforts. Work closely with cross-functional teams to communicate results to technical stakeholders and clients. Technical Skills & StackComputer Vision & Deep Learning (Core Focus): Libraries: PyTorch, OpenCV, scikit-image, Detectron2, MMSegmentation, and anomalib. Architectures: ResNet, Faster R-CNN, Swin Transformers, Autoencoders (SAE/VAE), and Masked Autoencoders (MAE). Concepts: Image classification, object detection, segmentation, basic CNN architectures Machine Learning & Data Science: Techniques: Supervised/Unsupervised learning, XGBoost, LightGBM, Random Forest, PCA/SVD (Dimensionality Reduction), and Cross-Validation. Libraries: Scikit-learn, XGBoost, Pandas, NumPy, SciPy, and Matplotlib. Data & Tools: Basic SQL knowledge Familiarity with Git or version control Experience & Education Experience: 4-5 years of overall experience, with recent handson work in Computer Vision projects for at least 1 year Education: Bachelors degree in Computer Science, Engineering, Mathematics, or related field Must-Have Requirements Handson experience with at least one deep learning framework (PyTorch or TensorFlow) Recent experience working on Computer Vision use cases Understanding of core computer vision concepts (CNNs, image preprocessing, model evaluation) Ability to write clean Python code for data processing and model development Strong willingness to learn and work in a fastpaced environment