Staff Data Scientist - Entity Resolution, IDGraph

Staff Data Scientist - Entity Resolution, IDGraph WHY SOCURE? Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading. ABOUT THE ROLE We are seeking a Staff Data Scientist to lead advanced data science and R&D efforts for the ID Graph, Socure’s foundational platform powering identity intelligence across our product ecosystem. This Staff-level role operates at platform scale, with responsibility extending beyond a single model or pipeline. You will work at the intersection of graph modeling, machine learning, and product innovation, collaborating closely with Engineering, Product Management, and multiple product teams. The ID Graph is the core intelligence backbone for many downstream products, and your work will directly impact Socure’s ability to deliver trusted, scalable, and explainable identity solutions. WHAT YOU'LL DO ENTITY RESOLUTION & GRAPH EVALUATION - Lead the evaluation and continuous improvement of entity resolution and entity linking pipelines. - Debug new builds, identify anomalies, and recommend modeling or system-level improvements. - Define, implement, and maintain scalable performance and quality metrics, leveraging automation and LLM-based approaches where appropriate. - Partner with Engineering to optimize entity linking and ranking systems using Learning-to-Rank and related techniques. - Design methods to assess and classify entity confidence and quality across the graph. DATA QUALITY & MODELING FRAMEWORKS - Design and implement a comprehensive data quality framework for graph-based identity data. - Translate abstract quality concepts (e.g., reliability, stability, consistency) into measurable signals. - Use data quality insights to guide modeling decisions, experimentation strategy, and product prioritization. SIGNAL DISCOVERY & GRAPH INTELLIGENCE - Identify and operationalize generalized, high-impact predictive signals derived from graph structure, temporal dynamics, and relational patterns. - Develop scalable approaches to link prediction, label propagation, and semi-supervised learning within the ID Graph. - Explore and evaluate advanced graph modeling techniques, including graph-based ML, knowledge graph methods, and Graph Neural Networks (GNNs), when appropriate. - Focus on durable abstractions rather than one-off features, ensuring solutions are explainable, compliant, and reusable across multiple products. CROSS-FUNCTIONAL COLLABORATION & TECHNICAL LEADERSHIP - Collaborate closely with Engineering, Product Management, Compliance, and downstream product teams. - Act as a technical leader within the Identity organization, influencing modeling standards, experimentation rigor, and best practices. - Translate complex technical findings into clear insights and recommendations for both technical and non-technical stakeholders. - Support the launch of new product capabilities built on top of the ID Graph. LEADERSHIP COMPETENCIES - Demonstrate strong ownership, strategic impact, and assertive communication. - Mentor peers, foster a culture of growth, and build authentic relationships across teams. - Embrace feedback, adapt resiliently to challenges, and pursue continual self-improvement. WHAT YOU BRING CORE TECHNICAL SKILLS - Strong proficiency in Python and PySpark. - Deep experience with: - Classification models - Learning-to-Rank - Anomaly Detection - Statistical Modeling - Experienc ... (truncated, view full listing at source)