Computer Vision Data Scientist
In your new role, you will:
• Analyze large-scale receipt data for fraud patterns and anomalies
• Develop statistical methods to detect subtle inconsistencies in receipt data
• Design feature engineering strategies combining OCR, visual embeddings, and behavioral signals
• Build and optimize ML models for fraud detection using collected data points
• Develop fraud scoring algorithms that combine multiple detection signals and model outputs
• Implement threshold optimization strategies balancing precision and recall for different risk levels
• Design comprehensive fraud scoring systems
• Develop weighted scoring mechanisms adaptive to fraud types and retailer patterns
• Create interpretable scoring frameworks for manual review teams
We're looking for:
• 4+ years as a data scientist with experience in fraud detection
• Strong expertise in hypothesis testing, time series, and anomaly detection
• Hands-on experience with classification, ensemble methods, and deep learning (scikit-learn, XGBoost, PyTorch/TensorFlow)
• Computer Vision - Strong experience with image processing and embedding, specifically EfficientNet and FAISS, is a plus
• Experience with high-volume transaction processing and real-time decision systems
• Knowledge of retail/e-commerce fraud patterns preferred
• Familiarity with document fraud techniques and anti-fraud methodologies
Why join us?
• Cutting-edge tech stack including GenAI and ML
• A global team with diverse perspectives
• 100% remote work
• Opportunity to influence product direction and company growth