Staff Data Scientist – Fraud & Risk

We’re hiring a Staff Data Scientist to join a high-performing Fraud & Risk Data Science team responsible for building and deploying the advanced machine learning models that power core fraud detection and identity risk products.

You’ll lead technical initiatives, mentor peers, and drive delivery of impactful solutions across fraud detection, risk management, and identity verification.

What You’ll Do

•      Design, develop, and implement advanced deep learning models including transformers, CNNs/RNNs, and graph learning algorithms to solve complex fraud and risk problems

•      Build and optimize models across diverse input types: tabular data, natural language, point clouds, and images

•      Lead the full ML lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and production monitoring

•      Take ownership of project outcomes, data quality, and delivery timelines

•      Stay current with advancements in AI and ML and apply innovative approaches to real-world problems

What You Bring

•      Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field or equivalent professional experience

•      8+ years of experience in data science, machine learning, or related fields, ideally in high-growth tech or fintech

•      Hands-on experience in fraud prevention, risk modeling, or identity verification

•      Demonstrated experience developing and deploying deep learning models: transformers, CNNs/RNNs, graph learning

•      Experience working with diverse data modalities: tabular data, text/language, point clouds, and images

•      Strong proficiency in Python, SQL, and major ML frameworks (PyTorch, TensorFlow, scikit-learn)

•      Deep understanding of ML algorithms, model evaluation techniques, and data pipeline development

•      Experience with model deployment and monitoring in production environments

•      Prior R&D or build-from-scratch modeling work

Nice to Have

•      Experience with real-time model inferencing

•      Experience with LLMs and Agentic AI frameworks (LangChain, LangGraph, Ray)