Senior Data Science - Integrity

Responsibilities

• Build and deploy supervised and unsupervised ML models for policy violation detection, counterfeit detection, and fraud detection.
• Build and grow the Integrity, Safety & Trust pod, mentor applied data scientists and deliver end-to-end projects with measurable business outcomes.
• Design feature pipelines that leverage product text, images, seller behavior, and transaction data.
• Apply NLP models for text classification, entity extraction, and multi-lingual moderation (Arabic + English).
• Utilize multimodal architectures (CLIP, ViT + BERT) for image text cross-validation.
• Develop graph-based and anomaly detection models to identify coordinated or suspicious merchant activity.
• Collaborate with product, legal, and operations teams to define integrity policies and feedback loops.
• Implement dashboards and monitoring for real-time detection and escalation (e.g., Elastic, Grafana).
• Optimize model precision/recall tradeoffs based on enforcement and user experience goals.
• Familiarity with graph learning, anomaly detection, and multimodal data pipelines.br> br>
• Bachelor s or Master s degree in Computer Science, Data Science, or a related field.
• 5+ years of experience in applied ML, with at least 2+ years focused on
• Trust & Safety, Integrity, or Fraud Detection systems.
• Experience with multi-modal text-image modeling (e.g., OCR, CLIP/ViT, layout analysis), taxonomy or attribute extraction, policy classification, and Arabic/English content moderation.
• Strong proficiency in Python, SQL, and ML libraries such as PyTorch, Transformers, Scikit-learn, and OpenCV.
• Experience developing streaming or near-real-time detection systems (Kafka, Redis Streams, or equivalent).
• Knowledge of e-commerce ecosystems, product policy enforcement, and counterfeit or low-quality detection is a plus.
• Excellent analytical reasoning, communication, and cross-functional collaboration skills; able to balance enforcement precision with business impact.