Job Description
We are looking for candidates with strong algorithmic capabilities and hands-on experience applying deep learning to fraud detection, abuse detection, or security-related problems.
Key Responsibilities
- Design and build machine learning / deep learning models for fraud and cheating detection
- Analyze large-scale behavioral and transactional datasets to identify abnormal patterns
- Develop scalable detection systems for fraud, abuse, or adversarial behaviors
- Apply advanced modeling approaches including deep learning architectures (e.g., Transformer-based models)
- Improve model performance through feature engineering, model optimization, and continuous evaluation
- Collaborate with engineering teams to deploy and maintain production-ready models
- Translate complex risk and security problems into practical data science solutions
Requirements
- 5+ years of experience in Data Science, Machine Learning, or related fields
- Strong hands-on experience with Deep Learning
- Experience in anti-fraud, anti-cheat, abuse detection, risk modeling, or security-related ML
- Strong algorithmic and modeling skills
- Proficiency in Python and common ML/DL frameworks
- Experience working with large-scale datasets and production models
- Ability to work independently in a highly technical environment
Preferred Qualifications
- Experience with Transformer-based models or advanced deep learning architectures
- Background in Trust & Safety, Fraud Detection, or Security Analytics
- Experience with anomaly detection or behavioral modeling systems
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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