Senior Data Scientist

Overview

Senior Anti-Fraud/Security Data Scientist responsible for safeguarding financial integrity by developing fraud detection models.

Responsibilities

• Feature Engineering and Selection: Identify, extract, and engineer features from diverse data sources to discriminate between legitimate and fraudulent users. Conduct feature selection and dimensionality reduction to optimize model performance.


• Model Development and Evaluation: Develop and implement ML models to detect fraud; evaluate with relevant metrics and conduct A/B tests to validate effectiveness.


• Data-Driven Insights: Analyze data to uncover patterns, trends, and anomalies that indicate fraudulent behavior; generate actionable insights to enhance prevention strategies.


• Model Deployment and Monitoring: Collaborate with engineering to deploy models into production and establish monitoring to track performance and detect concept drift.

Additional Responsibilities

• Cross-Functional Collaboration: Communicate technical concepts to both technical and non-technical stakeholders; collaborate with risk analysts and operational teams to refine prevention strategies.


• Team Leadership: Mentor and guide data scientists, fostering a culture of innovation and continuous learning.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.


• Strong proficiency in Python or R programming languages.


• Expertise in machine learning and statistical techniques applicable to security and fraud (e.g., anomaly detection, unsupervised learning, supervised classification, time-series analysis, graph analytics, fraud risk scoring).


• Experience with data mining, data cleaning and feature engineering in security/fraud contexts.


• Domain expertise in fraud detection, financial crime, or cybersecurity threat detection.


• In-depth knowledge of fraud detection methodologies and best practices, including rule-based systems, anomaly detection and behavioral analytics.


• Familiarity with security controls and threat modeling.


• Experience with risk scoring, alert triage, and investigation workflows; ability to create production-ready models with monitoring and explainability considerations.


• Excellent problem-solving and analytical skills; strong attention to detail.


• Ability to work independently and as part of a team; strong collaboration with security, risk, and engineering stakeholders.


• Minimum of 6 years of relevant experience in security/fraud data science or closely related fields.


• Research and development experience is a plus; experience publishing or presenting in security/fraud venues is advantageous.


• Proficiency with security/fraud tooling and platforms is a plus.