Computer Vision Data Scientist

About this 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