Data Scientist (Big Data R&D, Identity Graph & Deceased Monitoring)

Join Socure's Big Data R&D team as a Data Scientist, where you'll develop graph-based algorithms and data pipelines on massive PII datasets. You'll work closely with senior data scientists and engineers, contributing to the design and implementation of machine learning, data mining, statistical, and graph-based algorithms. This is a remote position with comprehensive benefits, including health coverage, parental leave, 401k, and a generous PTO policy.

• Contribuer à la conception et à la mise en œuvre d'algorithmes d'apprentissage automatique, d'exploration de données, statistiques et basés sur des graphes pour analyser de très grands ensembles de données.
• Construire et maintenir des composants de pipelines de traitement des données (ETL, génération de caractéristiques, normalisation) en utilisant des outils tels que Spark/PySpark et AWS.
• Analyser de grands ensembles de données pour aider à développer et à affiner les algorithmes de résolution d'entités et de correspondance d'identité qui alimentent les solutions de surveillance des décès et de conformité de Socure.

• Hands‑on experience with Spark or PySpark and common ML libraries (e.g., scikit‑learn, XGBoost, TensorFlow/PyTorch a plus)
• Solid experience writing and optimizing SQL for large datasets; comfort working in data lake / warehouse environments
• Working knowledge of supervised/unsupervised ML and basic statistics (similarity measures, clustering, evaluation metrics)
• Master’s degree with 2+ years of experience, or Ph.D. with 1+ years of experience in a data science or analytics role, or equivalent practical experience
• Bonus: experience with Elasticsearch or DynamoDB; workflow tools such as Airflow for automating data pipelines
• Exposure to graph techniques or graph databases (Neo4j, AWS Neptune, GraphFrames) is a strong plus
• Ability to break down loosely defined problems, ask good clarifying questions, and iterate quickly with feedback
• Familiarity with UNIX environments and the AWS ecosystem (e.g., EMR, S3); Databricks experience is a plus
• Proficiency in at least one general-purpose programming language used in data science (Python, or Scala)