Staff Data Scientist - Entity Resolution, IDGraph

📋 Description

• • Socure is at the forefront of building the identity trust infrastructure essential for the digital economy, dedicated to verifying 100% of good identities in real-time and proactively preventing fraud. Our mission is ambitious, tackling complex challenges with a profound impact on businesses, governments, and individuals daily.
• • We are seeking a highly skilled and experienced Staff Data Scientist to spearhead advanced data science and research and development initiatives for our critical ID Graph platform. This foundational platform underpins identity intelligence across Socure's entire product ecosystem.
• • As a Staff-level contributor, you will operate at a significant platform scale, with responsibilities extending beyond individual models or pipelines. Your role will be pivotal at the intersection of graph modeling, machine learning, and product innovation, requiring close collaboration with cross-functional teams including Engineering, Product Management, and various product development groups.
• • The ID Graph serves as the core intelligence backbone for numerous downstream products. Consequently, your contributions will directly influence Socure's capacity to deliver identity solutions that are not only trusted and scalable but also inherently explainable and compliant.
• • ENTITY RESOLUTION & GRAPH EVALUATION:
• • Lead the comprehensive evaluation and drive continuous improvement of our sophisticated entity resolution and entity linking pipelines. This involves a deep dive into existing processes to identify areas for enhancement and optimization.
• • Take ownership of debugging new builds, meticulously identifying anomalies, and formulating actionable recommendations for both modeling and system-level improvements to ensure peak performance and accuracy.
• • Define, implement, and rigorously maintain scalable performance and quality metrics. This includes leveraging automation and exploring advanced techniques, such as LLM-based approaches, to enhance efficiency and insight generation.
• • Partner closely with the Engineering team to optimize entity linking and ranking systems, employing state-of-the-art techniques like Learning-to-Rank and related methodologies to refine relevance and accuracy.
• • Design and implement robust methods to accurately assess and classify entity confidence and overall quality across the entire graph, ensuring a high degree of data integrity.
• • DATA QUALITY & MODELING FRAMEWORKS:
• • Architect and implement a comprehensive data quality framework specifically tailored for graph-based identity data. This framework will be crucial for maintaining the integrity and reliability of our core data assets.
• • Translate abstract, qualitative concepts of data quality—such as reliability, stability, and consistency—into concrete, measurable signals that can be tracked and acted upon.
• • Utilize the insights derived from data quality assessments to strategically guide modeling decisions, shape experimentation strategies, and inform product prioritization, ensuring development efforts are focused on areas of greatest need and impact.
• • SIGNAL DISCOVERY & GRAPH INTELLIGENCE:
• • Proactively identify and operationalize generalized, high-impact predictive signals. These signals will be derived from the intricate structure of the graph, its temporal dynamics, and complex relational patterns, unlocking new levels of predictive power.
• • Develop and implement scalable approaches for critical graph-based tasks, including link prediction, label propagation, and semi-supervised learning within the ID Graph environment.
• • Explore, evaluate, and potentially implement advanced graph modeling techniques. This includes investigating graph-based Machine Learning, knowledge graph methodologies, and Graph Neural Networks (GNNs) where they offer significant advantages.
• • Maintain a strategic focus on developing durable abstractions rather than isolated, one-off features. This ensures that all solutions are inherently explainable, compliant with relevant regulations, and reusable across multiple product lines, maximizing efficiency and consistency.
• • CROSS-FUNCTIONAL COLLABORATION & TECHNICAL LEADERSHIP:
• • Foster strong, collaborative relationships with Engineering, Product Management, Compliance, and various downstream product teams, ensuring alignment and shared understanding.
• • Serve as a key technical leader within the Identity organization, actively influencing modeling standards, promoting rigorous experimentation practices, and championing best practices across the team.
• • Skillfully translate complex technical findings into clear, concise, and actionable insights and recommendations, effectively communicating with both technical and non-technical stakeholders.
• • Provide essential support for the successful launch of new product capabilities that are built upon and leverage the power of the ID Graph.
• • LEADERSHIP COMPETENCIES:
• • Consistently demonstrate strong ownership, a commitment to strategic impact, and assertive, clear communication.
• • Actively mentor peers, cultivate a culture of continuous learning and growth, and build authentic, trusting relationships across diverse teams.
• • Embrace constructive feedback, adapt resiliently to challenges and changing priorities, and actively pursue ongoing self-improvement and professional development.