Data Scientist, KYC Anti Fraud

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

About the Role

We are looking for a Data Scientist to join the Risk Data Science team, focusing on building in-house KYC anti-fraud solutions.

The role

involves designing and developing AI/ML models to detect identity fraud, with a strong emphasis on ID forgery detection, document recognition, and image-based analysis. You will work closely with Risk, Engineering, Product, and Operations teams to deliver scalable and explainable fraud detection solutions in a fast-paced fintech environment.

Responsibilities Develop and maintain in-house KYC and anti-fraud solutions, including ID forgery detection, document recognition and verification, and image and video-based fraud analysis. Design, train, fine-tune, and evaluate computer vision and/or LLM-based models for fraud detection, covering image quality assessment, tampering and forgery artefacts, and AI-generated content or deepfake signals. Analyse large-scale, unlabeled or weakly labelled datasets to identify suspicious patterns and generate features for manual review and model training. Collaborate with engineering teams to deploy models into production pipelines, and work closely with Strategy and Operations teams to validate outputs and improve detection coverage. Monitor model performance in production and continuously iterate to improve coverage while maintaining low false-positive rates. Requirements Bachelor’s Degree or above in Computer Science, Data Science, AI, or a related field Strong experience in Python and common data science / ML libraries Solid understanding of CV/ML/AI fundamentals Experience with computer vision techniques (e.g. image preprocessing, feature extraction,  OCR and etc) Familiarity with model training, fine-tuning, and evaluation Ability to work with large-scale datasets and conduct exploratory data analysis Good problem-solving skills and ability to work independently in a fast-paced environment Preferred Experience in KYC, fraud detection, or risk-related domains, with hands-on expertise in ID forgery detection, OCR and document understanding, and image tampering or forgery analysis. Experience applying or fine-tuning LLMs for analysis, classification, or automation tasks, and working with unlabeled or imbalanced datasets. Knowledge of image quality assessment (IQA), frequency-domain analysis, and AI-generated content or deepfake detection techniques. Familiarity with AWS or cloud-based ML workflows, including SageMaker and batch or online inference pipelines. Ability to clearly explain technical results to non-technical stakeholders.