Responsibilities
- Oversee assessment and ongoing enhancement of systems that resolve and link identity entities.
- Investigate issues in new system versions, detect irregularities, and propose improvements at model or infrastructure levels.
- Create, deploy, and manage scalable metrics for performance and data quality, incorporating automation and large language models where applicable.
- Work with engineering teams to refine identity linking and ranking using Learning-to-Rank and similar methodologies.
- Develop techniques to evaluate and categorize the confidence and quality of identity nodes across the graph.
- Build and maintain a robust framework for monitoring data integrity within graph-based identity structures.
- Convert high-level quality attributes—such as consistency and reliability—into quantifiable indicators.
- Apply data quality analysis to inform model development, testing strategies, and product roadmap decisions.
- Discover and integrate broad-use predictive signals from graph topology, time-based patterns, and relational behaviors.
- Design scalable methods for predicting links, propagating labels, and enabling semi-supervised learning in the identity graph.
- Research and test advanced graph modeling strategies, including graph machine learning, knowledge graphs, and Graph Neural Networks when relevant.
- Prioritize reusable, long-term solutions over temporary features, ensuring outputs are interpretable, compliant, and adaptable across products.
- Collaborate closely with engineering, product, compliance, and downstream teams to align technical and business goals.
- Serve as a technical leader within the identity domain, shaping modeling standards and experimental discipline.
- Communicate complex analytical results clearly to both technical and non-technical audiences.
- Support the deployment of new product features powered by the identity graph.
- Demonstrate accountability, strategic influence, and proactive communication.
- Guide team members, promote a learning culture, and strengthen cross-functional relationships.
- Welcome feedback, adapt effectively to obstacles, and pursue ongoing personal and professional growth.
Team
Cross-functional collaboration with Engineering, Product Management, Compliance, and downstream product teams
