Computer Vision Data Scientist

Stack

Responsibilities

• ▹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
• ▹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

Requirements

• ▹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 embeddings
• ▹Experience with high-volume transaction processing and real-time decision systems

Nice to have

• ▹Experience with EfficientNet and FAISS
• ▹Knowledge of retail/e-commerce fraud patterns
• ▹Familiarity with document fraud techniques and anti-fraud methodologies

What we offer

• ▹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