Computer Vision Data Scientist

EER Poland; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

100% remote work

GenAI stack exposure

Global team

Job summary

EER Poland is seeking a Computer Vision Data Scientist to analyze large-scale receipt data for fraud patterns, develop statistical methods, and design feature engineering strategies that combine OCR, visual embeddings, and behavioral signals. You will build ML models for fraud detection and develop scoring systems that balance precision and recall for different risk levels.

You will work with a global team in a 100% remote setup, applying cutting-edge techniques including EfficientNet, FAISS,

Qualifications

• 4+ years as a data scientist with fraud detection experience.
• Strong expertise in hypothesis testing, time series and anomaly detection.
• Hands-on experience with classification, ensemble methods and deep learning.
• CV experience with image processing and embeddings (EfficientNet) and real-time decision systems is a plus.
• Familiarity with retail/e-commerce fraud patterns and document fraud techniques.

Responsibilities

• Analyze large-scale receipt data for fraud patterns and anomalies.
• Develop statistical methods to detect 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 signals and model outputs.
• Implement threshold optimization balancing precision and recall for 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.

Skills

Fraud detection

Hypothesis testing

Time series

Anomaly detection

Classification

Ensemble methods

Deep learning

scikit-learn

XGBoost

PyTorch

TensorFlow

Tools

EfficientNet

FAISS

Job description

Computer Vision Data ScientistIn 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