Staff Data Scientist, Fraud & Risk

Quick Facts

• Role: Staff Data Scientist (Fraud & Risk)

Description

Design, build, and optimize advanced deep learning models that power fraud detection and risk management, including identity verification use cases. Lead the end-to-end machine learning lifecycle from data exploration and feature engineering through training, evaluation, deployment, and monitoring in production. Mentor peers, research new data sources and algorithms, and present findings to technical and executive stakeholders.

Responsibilities

• Design, develop, and implement advanced deep learning models (transformers, CNNs/RNNs, and graph learning algorithms)

• Build and optimize models across multiple data modalities (tabular, natural language, point clouds, images)

• Own the full ML lifecycle: exploration, feature engineering, training, evaluation, deployment, and monitoring

• Take ownership of project outcomes, data quality, and delivery timelines; escalate and resolve issues proactively

• Mentor and share knowledge with peers and junior data scientists

• Partner cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights

• Research new data sources and develop novel algorithms to advance fraud detection

• Present recommendations clearly to technical and executive audiences

• Stay current with AI/ML advances and apply innovative approaches to real-world problems

Requirements

• Master’s or PhD in a relevant field (Computer Science, Statistics, Applied Mathematics, Data Science) or equivalent experience

• 8+ years in data science/ML (ideally in a high-growth tech or fintech environment)

• Experience in fraud prevention, risk modeling, or identity verification

• Hands-on deep learning model development and deployment (transformers, CNNs/RNNs, graph learning)

• Experience with diverse data modalities (tabular, text/language, point clouds, images)

• Strong Python and SQL

• Strong ML libraries/frameworks knowledge (PyTorch, TensorFlow, scikit-learn)

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

• Experience deploying and monitoring models in production (real-time inference is a plus)

• LLMs and agentic AI framework/infrastructure experience is a plus

• Proactive delivery ability, mentoring, and cross-functional influence

• Strong communication skills for both technical and non-technical stakeholders

• Commitment to continuous learning and high ethical standards

Benefits

Compensation range: $191K - $230K