Senior Data Scientist (Big Data R&D, Identity Graph & KYC)

Join Socure, a leading provider of digital identity verification and fraud prevention solutions. As a Senior Data Scientist, you will lead the design and deployment of advanced machine learning and graph algorithms on large-scale PII datasets. You will own end-to-end projects, define standards for feature engineering and data quality, and partner closely with product managers and engineers. This is a fully remote position with a comprehensive benefits package.

• Lead the design and deployment of advanced machine learning and graph algorithms on large-scale PII datasets, owning end-to-end projects from problem definition through production validation.
• Architect and optimize graph-based identity representations to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models.
• Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets.
• Demonstrated ability to lead medium‑to‑large projects end‑to‑end, make sound trade‑off decisions under ambiguity, and influence cross‑functional stakeholders with data and clear reasoning
• Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent high‑stakes domains is a plus
• Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred
• Experience developing production-quality data pipelines and automated workflows using Airflow or similar orchestration tools
• Solid SQL skills and experience working with large-scale analytical data stores
• Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikit‑learn, XGBoost, TensorFlow or PyTorch
• Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification)
• Master’s degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles; background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred
• Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets