Staff Data Scientist - Fraud & Risk

About the role

We are seeking a skilled and motivated Staff Data Scientist to join our Fraud & Risk Data Science team. As an advanced-level individual contributor, you will design, build, and optimize advanced DS/ML models that power our core fraud detection and risk management solutions. You will lead technical initiatives, mentor peers, and drive functional productivity and project success.

Responsibilities

• Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges.
• Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images.
• Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
• Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges.
• Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning.
• Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions.
• Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection.
• Present findings and recommendations to technical and executive stakeholders with clarity and influence.
• Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems.

Requirements

• 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 a high-growth tech or fintech environment.
• Experience in fraud prevention, risk modeling, or identity verification.
• Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs/RNNs, and graph learning).
• Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images.
• Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development.
• Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus).
• Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus.
• Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions.
• Excellent communication skills with the ability to translate complex data problems into actionable business insights for both technical and non-technical audiences.

Qualifications

• Commitment to continuous learning, professional integrity, and high standards of business ethics.

Skills

• Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development.
• Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus).
• Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus.

Benefits

Commensurate with experience.

Pay

$180K - $210K

Schedule

TBD