Computer Vision Data Scientist

EER Poland eerpoland.com Open

Remote Poland Full-time Mid-level AI Research

At a glance

Mid-level AI Research role at EER Poland. Remote, Poland · full-time.

RoleAI Research

SeniorityMid-level

LocationPoland

WorkplaceRemote

EmploymentFull-time

PostedDec 1, 2025

Role brief

remoteroles summary, based on the employer's posting.

Inspect receipt data at scale to find fraud signals, unusual patterns and subtle inconsistencies
Build features from OCR, visual embeddings and behavior data for fraud detection models
Train and improve machine-learning models, then combine their outputs into fraud scores
Tune score thresholds to balance precision and recall across different risk levels
Create adaptable scoring approaches that reflect fraud types and retailer patterns and help manual reviewers interpret results

At least four years working as a data scientist, including fraud detection experience
Strong command of hypothesis testing, time-series methods and anomaly detection
Practical experience with classification, ensemble approaches and deep learning, using the listed frameworks
Experience processing high-volume transactions and supporting real-time decisions

This role suits a mid-level data scientist focused on computer vision and fraud detection. It is fully remote in Poland.

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?