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 WOR
K
Machine Learnin
- g: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 characteristic
- s.Model Optimization: Experience with hyperparameter tuning, model compression, and latency optimization for production-grade system
- s.Experimentation & Evaluation: Strong grasp of experimental design, A/B testing, and evaluation metrics tailored to imbalanced fraud dataset
s.
Programming and Syst
- em:Production-Grade Code: Ability to write clean, maintainable, and well-tested Python code suitable for production environmen
- ts.ML Pipelines: Experience designing and building scalable data and ML pipelines for feature engineering, training, and inferen
- ce.Performance Optimization: Familiarity with profiling and optimizing code for large-scale data processi
ng.
Computer Vision and Document Intellige
- nce:Document Processing: Experience with OCR technologies and document layout analysis for automated information extract
- ion.Object Detection: Familiarity with object detection frameworks (e.g.,Detectron2) for ID document parsing and verificat
- ion.Image Processing: Proficiency in image preprocessing, quality assessment, and feature extraction from visual d
ata.
Identity Verification and Biomet
- rics:Face Recognition: Experience with facial recognition and face-matching algorithms for identity verifica
- tion.Liveness Detection: Familiarity with anti-spoofing and liveness detection techniques (e.g., passive/active liven
- ess).ID Document Verification: Understanding of ID authenticity checks, tamper detection, and document foren
sics.
Additional S
- kills:Network/Graph Analysis: Experience with graph-based techniques for identifying fraud rings and interconnected pat
- terns.NLP and LLMs: Ability to leverage large language models and NLP techniques for document understanding, entity extraction, and unstructured data proce
- ssing.Cloud Computing (Plus): Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for model deployment and scalable processing is a
plus.
REQUIRED QUALIFI
- CATIONSBachelor's degree in Data Science, Computer Science, Statistics, or a related
- field.Strong proficiency in Python or R programming lan
- guages.Expertise in machine learning algorithms (e.g., decision trees, random forests, gradient boosting, neural net
- works).Experience with data mining and data cleaning tech
- niques.Knowledge of fraud detection methodologies and best pra
- ctices.Excellent problem-solving and analytical
- skills.Ability to work independently and as part of
- a team.Having at least 6 years of relevant exp
- erienceResearch and development experience is
- a plusExperience in identity verification, KYC, or digital onboarding is
a plus.
