Lead Data Scientist - Integrity & Safety

Protect millions of buyers and sellers as our Senior Data Scientist for Integrity & Trust. You'll lead building the AI defense systems that detect fraud, eliminate counterfeit products, and maintain marketplace quality across our platform. In MENA's high-COD environment (75% of transactions), trust is everything. Your models will be the difference between platform growth and reputation damage.

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.
  

Requirements

• 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.
• Proven experience building or growing a team of applied data scientists and delivering end-to-end projects with measurable business outcomes.
• Excellent analytical reasoning, communication, and cross-functional collaboration skills; able to balance enforcement precision with business impact.

Benefits

• Medical Health Insurance
• Performance Bonus
• Others