data
Posted Feb 4Staff Data Scientist - Fraud & Risk
at Socure
United StatesHybrid
Responsibilities
- - 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.
- - 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.
Requirements
- 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 work hands-on with advanced deep learning models, driving delivery of impactful solutions for 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 address complex fraud and risk challenges.
- - Stay current with advancements in AI and machine learning, applying 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 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