Staff Data Scientist - Riskified

Education: M.Sc. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field. Experience: 6+ years of hands-on experience designing, training, and deploying complex machine learning models in large-scale production environments. Deep Learning & SOTA Models: Proven expertise with Transformers, Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), or Advanced Tabular Learning architectures. Big Data Ecosystem: Strong practical experience working with large-scale distributed data processing systems (e.g., Spark, Databricks) and graph databases/frameworks. Technical Influence: Proven track record of technical leadership, setting architectural direction, and translating complex business requirements into high-impact algorithmic solutions.

The Data Science department powers Riskified’s core engine by building production-grade algorithmic solutions at immense scale. As a Staff Data Scientist on the Identity team, you will drive the innovation roadmap for our Identity Graph Engine. You will architect, research, and deploy state-of-the-art machine learning models - spanning graph neural networks, transformers, and deep tabular architectures - to solve complex entity resolution and fraud prevention challenges at scale.

Education: M.Sc. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field. Experience: 6+ years of hands-on experience designing, training, and deploying complex machine learning models in large-scale production environments. Deep Learning & SOTA Models: Proven expertise with Transformers, Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), or Advanced Tabular Learning architectures. Big Data Ecosystem: Strong practical experience working with large-scale distributed data processing systems (e.g., Spark, Databricks) and graph databases/frameworks. Technical Influence: Proven track record of technical leadership, setting architectural direction, and translating complex business requirements into high-impact algorithmic solutions.