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Job description

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

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