Staff Data Scientist - Fraud & Risk
New York, New York, United StatesAll locationsNew York, New York, United StatesHybrid - San Francisco, CASeattle, Washington, United StatesMiami, Florida, United States Hybrid
$180,000–$210,000 a yearJobFig found this opening at its original source and checks that it remains available.
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. You will work hands-on with advanced deep learning models, driving delivery of impactful solutions for fraud detection, risk management, and identity verification. This role requires deep technical expertise, strategic ownership, and a commitment to Socure’s leadership principles, including continuous learning, effective communication, and accountability.
What you'll 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
- 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
- Experience with LLMs and Agentic AI framework/infrastructure (e.g.
- 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.
- Commitment to continuous learning, professional integrity, and high standards of business ethics.