Data Scientist

• Rapidly prototype and benchmark multiple computer vision approaches for anomaly and defect detection including supervised detection and classification unsupervised and semi-supervised anomaly detection and modern foundation model based techniques on representative inspection imagery
• Build fair and reusable evaluation frameworks so that comparisons across methods are repeatable and credible across different asset types and defect categories
• Identify top-performing methodologies and clearly document trade-offs including data requirements performance complexity and inference cost
• Package the selected approach into a reusable template including reference code data preparation guidance training and evaluation patterns and supporting documentation so internal teams can extend it to additional use cases
• Collaborate closely with data scientists domain experts and stakeholders to ensure experiments are grounded in real inspection workflows and operational needs
• Communicate findings recommendations and trade-offs clearly to both technical and non-technical audiences
• Maintain thorough and organized documentation of experiments results and deliverables to ensure long-term usability beyond the engagement

What You Bring

• Masters degree or PhD in computer science machine learning computer vision engineering mathematics statistics applied sciences or a related quantitative field or equivalent experience
• Six or more years of experience in computer vision machine learning or related analytical product development with strong emphasis on deep learning based vision systems
• Proven ability to rapidly evaluate and down-select between competing modeling approaches using fair comparisons and evidence-based conclusions
• Strong knowledge of modern computer vision techniques including object detection classification and at least one relevant area such as anomaly detection few-shot learning or vision foundation models
• Solid understanding of evaluation methods for rare-event and class-imbalanced problems
• Advanced programming skills in Python and PyTorch or similar frameworks with experience working in version-controlled development environments
• Demonstrated ability to produce reusable well-documented code and frameworks for other data scientists
• Strong analytical problem-solving communication and documentation skills with the ability to engage effectively across technical and business audiences

Desired Qualifications

• Experience with anomaly detection in industrial infrastructure or visual inspection contexts
• Familiarity with vision foundation models for transfer learning embedding-based retrieval or low-supervision settings
• Experience working with large-scale image datasets and cloud-based machine learning platforms such as AWS Azure GCP or similar
• Background in infrastructure industrial inspection manufacturing quality assurance or other environments where image-based analytics drive operational decisions