Develop Analysis Frameworks: Design and maintain scalable frameworks that allow the DSCA team to perform fast, effective client resolutions during POCs, onboarding, and for existing production customers.
Bridge R&D and Operations: Act as the primary technical liaison between Watchlist DS and DSCA, translating complex model behaviors into actionable insights for customer-facing teams.
Build Monitoring & Alerting Systems: Create and manage mission-critical dashboards and real-time alert systems to monitor model performance, identify drift, and surface false positives/negatives at a sub-segment level.
Drive Closed-Loop Model Improvement: Systematically monitor customer production data and POC findings to integrate real-world feedback back into the core model development cycle.
Enhance In-House Pipelines: Build and optimize end-to-end data pipelines (using Spark and Airflow) that support custom reporting and automated performance tracking for Watchlist POCs.
Collaborate Cross-Functionally: Work closely with Product, Engineering, and Compliance to translate customer needs and regulatory requirements into measurable machine-learning objectives.

Bachelor’s degree in a quantitative field (Computer Science, Statistics, Mathematics, or similar) with 3–5 years of experience —or— a Master’s/Ph.D. with 1–3 years of experience.
Strong programming skills in Python and SQL, with hands-on experience in Spark and Databricks.
Experience with Airflow for orchestration and a working knowledge of Infrastructure as Code (e.g., Terraform) and AWS environments.
Solid grasp of descriptive statistics, hypothesis testing, and model evaluation metrics (Precision, Recall, F-beta, ROC-AUC).
Practical experience in data curation, labeling strategies, and building automated data-quality validation checks.
Excellent ability to communicate complex technical findings to both R&D peers and non-technical stakeholders.
A strong interest in NLP, LLMs, and staying ahead of the curve in compliance and regulatory technology.
Experience with Elasticsearch and real-time data indexing is preferred.
Background in AML (Anti-Money Laundering), Sanctions Screening, or Identity Fraud prevention is preferred.
Prior experience in a "Customer Data Science" or "Solutions DS" role where you built tools for internal or external stakeholders is preferred.

Data Scientist II – Global Watchlist

Key skills