Data Scientist - Fraud Strategy

About the job Data Scientist - Fraud Strategy


We are seeking a highly analytical and experienced Data Scientist Business Analyst to join our Fraud Strategy Practice. This role requires development, implementation and monitoring of fraud strategies. It involves utilizing statistical and predictive analytics, machine learning, and data visualization to design prevention strategies, provide actionable insights to business partners for strategic decision-making.

• Support the development and enhancement of fraud detection strategies across various financial products (e.g., new account opening, account takeover, money movement in retail or card).
• Develop and tune fraud rules within platforms such as ThreatMetrix or other decisioning systems.
• Use data (structured and unstructured) to support fraud pattern identification and mitigation efforts.
• Create and maintain dashboards and reporting tools in Power BI, Tableau, or similar platforms to communicate fraud trends and strategy performance.
• Monitor the performance of fraud strategies by tracking KPIs such as detection rate, prevention rate, false positives, and loss capture.
• Conduct initial analyses and assist in root cause investigations for fraud incidents or false positive spikes.
• Collaborate with teams such as Fraud Operations, Product, Engineering, and external partners to gather inputs and support fraud strategy execution.
• Participate in validation of new rules, tools, and models to ensure alignment with fraud risk mitigation goals. Document processes, analytics steps, and findings clearly to contribute to internal knowledge sharing and reviews.
• Stay updated on industry fraud trends and evolving attacker techniques; share relevant learnings with the broader team.

• We are looking for someone who is passionate about solving complex challenges, thrives in a fast-paced environment, and brings a strong foundation in fraud management and analytics.

• 4+ years related work experience in fraud strategy or fraud data science field with building strategies or models in banking industry (Retail Bank, Consumer Cards).
• Understanding of statistical methods, including classical statistics, probability theory, econometrics, and time-series analysis.
• High proficiency with data extraction and manipulation with SQL and Python. Working knowledge of Hive, Spark, AWS Sagemaker.
• Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
• Proven expertise in fraud rule development and strategy implementation with LexisNexis fraud solutions or comparable solutions (e.g., Actimize, etc).
• Strong verbal and written communication skills, especially in client-facing or cross-functional settings.

• Experience with ThreatMetrix rule creation and management is a big plus.
• Experience with machine learning is a plus.
• Background in consulting or client advisory roles in fraud strategy.