About the role
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