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

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

Originally posted on Himalayas

Computer Vision Data Scientist

Job Description