Computer Vision Data Scientist

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 Originally posted on Himalayas