As a Senior Anti-Fraud Data Scientist, you will be a key individual contributor responsible for safeguarding our company's financial integrity. You will leverage your deep expertise in data science and machine learning to design, develop, and implement advanced fraud detection models. You will also contribute to automating identity verification workflows, reducing manual review burden while maintaining accuracy and compliance standards. As a senior IC, you will provide technical leadership and mentorship while driving innovation in fraud prevention strategies. Your work will directly contribute to mitigating financial losses and protecting our customers.
NATURE OF WORK
Machine Learning:
- Advanced Model Design: Ability to architect end-to-end ML systems for fraud detection, selecting and combining appropriate algorithms (e.g., ensemble methods, deep learning, anomaly detection) based on problem characteristics.
- Model Optimization: Experience with hyperparameter tuning, model compression, and latency optimization for production-grade systems.
- Experimentation & Evaluation: Strong grasp of experimental design, A/B testing, and evaluation metrics tailored to imbalanced fraud datasets.
Programming and System:
- Production-Grade Code: Ability to write clean, maintainable, and well-tested Python code suitable for production environments.
- ML Pipelines: Experience designing and building scalable data and ML pipelines for feature engineering, training, and inference.
- Performance Optimization: Familiarity with profiling and optimizing code for large-scale data processing.
Computer Vision and Document Intelligence:
- Document Processing: Experience with OCR technologies and document layout analysis for automated information extraction.
- Object Detection: Familiarity with object detection frameworks (e.g.,Detectron2) for ID document parsing and verification.
- Image Processing: Proficiency in image preprocessing, quality assessment, and feature extraction from visual data.
Identity Verification and Biometrics:
- Face Recognition: Experience with facial recognition and face-matching algorithms for identity verification.
- Liveness Detection: Familiarity with anti-spoofing and liveness detection techniques (e.g., passive/active liveness).
- ID Document Verification: Understanding of ID authenticity checks, tamper detection, and document forensics.
Additional Skills:
- Network/Graph Analysis: Experience with graph-based techniques for identifying fraud rings and interconnected patterns.
- NLP and LLMs: Ability to leverage large language models and NLP techniques for document understanding, entity extraction, and unstructured data processing.
- Cloud Computing (Plus): Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for model deployment and scalable processing is a plus.
REQUIRED QUALIFICATIONS
- Bachelor's degree in Data Science, Computer Science, Statistics, or a related field.
- Strong proficiency in Python or R programming languages.
- Expertise in machine learning algorithms (e.g., decision trees, random forests, gradient boosting, neural networks).
- Experience with data mining and data cleaning techniques.
- Knowledge of fraud detection methodologies and best practices.
- Excellent problem-solving and analytical skills.
- Ability to work independently and as part of a team.
- Having at least 6 years of relevant experience
- Research and development experience is a plus
- Experience in identity verification, KYC, or digital onboarding is a plus.
