Computer Vision Data Scientist
Remote Poland Mid-level AI Research
At a glance
Mid-level AI Research role at EER Poland. Remote, Poland.
Pay not stated AI Research salary
RoleAI Research
SeniorityMid-level
LocationPoland
WorkplaceRemote
PostedDec 1, 2025 · 10mo ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Examine receipt records at scale to find fraud signals and unusual patterns
- Create statistical approaches for spotting small discrepancies in receipt data
- Build detection features from OCR results, image representations and behavioral data
- Train and improve fraud models, then combine their outputs into scoring methods
- Tune risk thresholds and develop explainable scores for different fraud types and review needs
What you bring
- At least four years working as a data scientist, including fraud detection experience
- Strong grounding in hypothesis tests, time-series analysis and anomaly detection
- Practical use of classification, ensemble approaches and deep learning, with scikit-learn, XGBoost or PyTorch/TensorFlow
Who this fits
This role suits a mid-level data scientist with experience in fraud modeling and receipt-focused computer vision work. It is fully remote within Poland.
From the employer
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