Senior Data Scientist - Fraud Data Infrastructure & Automation

Responsibilities

• Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems).
• Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows.
• Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases.
• Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection.
• Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines.
• Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long-term value.
• Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights.
• Conduct in-depth research to explore new data sources and develop novel algorithms and features.
• Lead the end-to-end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post-deployment monitoring.
• Present findings, trade-offs, and recommendations to technical and executive stakeholders.
• Mentor and share knowledge with peers and junior data scientists.

Requirements

• Education: Master's or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or related quantitative field (or equivalent experience).
• Experience: 5+ years in data science, machine learning, or related roles; experience in fraud prevention, risk modeling, or identity verification; working with large, messy datasets; diverse data modalities (tabular, text, point clouds, images).
• Technical Skills: Strong proficiency in Python and SQL; major ML libraries (PyTorch, TensorFlow, scikit-learn); machine learning algorithms and evaluation (AUC, lift, calibration); data pipelines in distributed environments (Spark, Airflow, Databricks); evaluating third-party data vendors.
• Preferred: Experience with LLMs and agentic AI frameworks (LangChain, LangGraph, Ray).
• Excellent communication skills; ability to lead technical workstreams and influence cross-functionally; commitment to continuous learning.