socure - Staff Data Scientist, Watchlist

• Data Quality & Enrichment • Improve the quality, coverage, and freshness of Watchlist's underlying data through next-generation ingestion pipelines. • Design and execute rigorous data quality analysis pipelines to identify anomalies, evaluate dataset health, and ensure high-fidelity inputs for downstream model training. • Apply NLP and AI to classify and enrich raw source data into normalized schemas — extracting structured entity attributes from unstructured sanctions, PEP, adverse media, and enforcement sources. • Expand multilingual capabilities to support global screening across Latin and non-Latin scripts. • Entity Resolution • Build and improve NLP systems that consolidate how watchlist identities are represented. Developing Information Extraction and Named Entity Recognition (NER) pipeline to deduplicate entities across lists and resolve aliases into canonical profiles.. • Develop approaches to handle how entity profiles change over time as names, aliases, and sanctions status evolve. • Measure and benchmark entity resolution quality, driving continuous improvement in coverage and accuracy. • Match Engine & Risk Scoring • Design and scale advanced NLP models and algorithms that perform real-time name matching and identity classification across diverse, multilingual unstructured data sources. • Build multi-signal risk scoring that combines name similarity, entity type, geography, list type, and other attributes into unified, calibrated risk scores. • Maintain and improve benchmarking frameworks, golden datasets, and regression tests that keep the match engine at the highest levels of recall and precision. • Analytics, Tuning & Evaluation • Build models and analytics that help customers tune their screening thresholds to the right operating point for their risk appetite and entity mix. • Develop backtesting and counterfactual analysis capabilities so customers and internal teams can understand how model or threshold changes would affect screening outcomes. • Design evaluation frameworks for AI-powered autonomous decision systems — defining correct behavior, calibrating confidence thresholds, and monitoring for drift in production. • AML Risk Detection • As Watchlist expands into payment screening, build the mathematical analysis and feature engineering needed to detect AML risk patterns across transaction data and payment message fields. • Develop and maintain the AML taxonomy and risk signal library that underlies Watchlist's classification and detection capabilities. • Apply graph-based methods to surface indirect risk exposure — identifying entities connected to sanctions risk even when they are not directly listed. • Research & Technical Leadership • Lead technical initiatives across Watchlist Data Science and shape the team's long-term approach to entity matching, enrichment, and AI. • Collaborate closely with Product and Engineering to translate research into production-grade systems at scale. • Stay current with advances in NLP, large language models, and entity resolution; prototype and deploy relevant techniques (e.g., advanced NER, LLM-based extraction) to AML use cases. • Mentor peers and contribute to a culture of technical rigor and continuous improvement.