Use data quality insights to guide modeling decisions, experimentation strategy, and product prioritization.
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.
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.
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.
Requirements
Master’s or PhD in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field
5+ years of experience in applied data science, machine learning, or artificial intelligence, with a focus on graph-based modeling and large-scale data systems
Strong proficiency in Python and PySpark
Deep experience with Classification models, Learning-to-Rank, Anomaly Detection, Statistical Modeling
Experience building and maintaining production-grade ML systems at scale
Hands-on experience with Databricks
Familiarity with graph databases and query languages such as NeptuneDB and OpenCypher
Experience with graph processing frameworks (e.g., GraphFrames)