Senior Data Scientist (International eKYC, Identity Graph)

Join Socure, a leading provider of digital identity verification solutions. As a Senior Data Scientist, you will drive the development of global identity verification solutions, design and deploy machine learning and graph-based systems, and lead complex cross-product initiatives. You will collaborate with various teams to launch and scale eKYC solutions across multiple countries and regions. This is a fully remote position with comprehensive benefits.

• Conception et déploiement de systèmes d'apprentissage automatique et basés sur des graphes adaptés à divers marchés internationaux.
• Responsabilité pour des initiatives complexes et transversales, telles que l'évolution du graphe d'identité international et le rapprochement probabiliste.
• Collaboration étroite avec les équipes Produit, Ingénierie, Conformité et GTM pour lancer et développer des solutions eKYC.
• 6+ years of hands-on applied ML / data science experience (4+ with Ph.D.), including owning production models and pipelines in high‑stakes domains (fraud, risk, identity, payments, credit, or similar)
• Significant prior work on international or multi‑region products is strongly preferred (e.g., cross‑country KYC, credit risk, payments, or compliance systems)
• Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience
• Feature engineering for noisy/heterogeneous identity data
• Proven expertise with graph technologies (e.g., Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms (entity resolution, link prediction, community detection, label propagation) at scale
• Expert‑level proficiency in Python and SQL, with extensive experience in distributed data processing (Spark/PySpark, Databricks or similar) on very large datasets
• Robust evaluation under label sparsity and feedback delays
• Deep experience designing, training, and deploying models for classification, ranking, anomaly detection, and/or graph learning, including:
• Calibration and thresholding tailored to regional risk and regulatory constraints