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