Senior Data Scientist

NATURE OF WORK

As a Senior Anti-Fraud/Security Data Scientist, you will play a pivotal role in safeguarding our company's financial integrity. You will leverage your expertise in data science and machine learning to develop and implement advanced fraud detection models. Your work will directly contribute to mitigating financial losses and protecting our customers.

Core Responsibilities

• Feature Engineering and Selection:
• Identify, extract, and engineer relevant features from diverse data sources (e.g., customer data, transaction data, behavioral data) to effectively discriminate between legitimate and fraudulent users.
• Conduct feature selection and dimensionality reduction techniques to optimize model performance and computational efficiency.
• Model Development and Evaluation:
• Develop and implement advanced machine learning models (e.g., anomaly detection, supervised classification, time series analysis) to accurately detect and prevent fraudulent activities.
• Rigorously evaluate model performance using appropriate metrics (e.g., precision, recall, F1-score, AUC-ROC) and conduct A/B testing to validate effectiveness.
• Data-Driven Insights:
• Analyze raw data to uncover patterns, trends, and anomalies that may indicate fraudulent behavior.
• Generate actionable insights and recommendations to enhance fraud prevention strategies.
• Model Deployment and Monitoring:
• Collaborate with engineering teams to deploy and operationalize developed models into production environments.
• Establish robust monitoring and alerting systems to track model performance, detect concept drift, and ensure ongoing effectiveness.

Additional Responsibilities

• Cross-Functional Collaboration:
• Effectively communicate technical concepts and findings to both technical and non-technical stakeholders.
• Collaborate with subject matter experts, risk analysts, and operational teams to understand fraud trends, identify emerging threats, and refine prevention strategies.
• Team Leadership:
• Mentor and guide data scientists to develop their skills and contribute to the team's success.
• Foster a culture of innovation, experimentation, and continuous learning within the team.

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.