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