Staff Data Scientist, Watchlist

About the role

We are looking for a Staff Data Scientist to join Socure's Watchlist Data Science team. Watchlist sits at the heart of global AML compliance — our platform screens hundreds of millions of entities in real time across sanctions lists, PEP databases, and adverse media sources for banks, fintechs, and payment companies worldwide. As a Staff Data Scientist, you will work on the hardest problems in entity matching and classification: scaling our patented real-time matching engine, building advanced Natural Language Processing (NLP) models for Named Entity Recognition (NER) and Information Extraction, and bringing next-generation research to production. This is a senior individual contributor role with broad technical ownership and direct impact on a product that helps the world's financial institutions manage sanctions and AML risk.

Responsibilities

• 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.
• Build and improve NLP systems that consolidate how watchlist identities are represented.
• Develop 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.
• 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.
• 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.
• 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.
• 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.

Qualifications

• Master's or PhD in Computer Science, Computational Linguistics, Statistics, Applied Mathematics, or a related field; or equivalent professional experience.
• 7+ years of experience in data science or machine learning, with meaningful work in NLP, entity resolution, or information extraction.
• Experience in AML, sanctions screening, adverse media, or financial crime detection is strongly preferred.
• Hands-on experience building and deploying NLP pipelines for entity extraction, named entity recognition, and record linkage at production scale.
• Familiarity with multilingual NLP and non-Latin script processing is a strong plus.
• Experience with LLMs and agentic AI frameworks (e.g., LangChain/LangGraph) is a plus.
• Strong proficiency in Python and major ML libraries (PyTorch, spaCy, HuggingFace Transformers).
• Strong SQL proficiency and experience with large-scale data pipelines and production ML systems.
• Excellent communication skills — able to translate model performance tradeoffs