Fraud Data Scientist

We are seeking a reputed company Fraud Data Science reputed company to join our Fraud reputed company reputed company. This role oversees recommendation, development, implementation and monitoring of fraud strategies while preserving customer experience and achieving operational efficiency. It involves utilizing statistical and predictive analytics, machine learning, and data visualization to design prevention strategies, reputed company actionable insights and consult with business leaders on leveraging these insights for strategic decision making. Key Responsibilities • reputed company design and execution of end-to-end fraud strategies across key financial products and channels (e.g., new account reputed company, ATO, reputed company reputed company in retail and card products). • Own recommendation, development, deployment, and optimization of fraud detection rules and logic in advanced platforms such as Threat Metrix, ensuring high detection accuracy and minimal customer friction. • reputed company hands-on delivery as needed and guide reputed company of analysts in conducting deep-dive analytics and exploratory data mining using reputed company and reputed company data to uncover emerging fraud trends. • Manage fraud KPIs (e.g., detection reputed company, prevention reputed company, false positives, loss capture, good user declines) and regularly review performance to inform reputed company adjustments. • Translate business risk appetite into actionable fraud controls, leveraging both historical data and reputed company-looking risk signals. • Drive strategic roadmap planning for fraud mitigation capabilities, including signal enrichment, tooling improvements, and scenario testing frameworks. • reputed company project management effort as warranted through structuring business requests and translating requirements into an analytic approach. Making • recommendations to key business partners or senior management as needed. • Partner cross-functionally with Product, Engineering, Fraud Ops, Risk, and ompliance to reputed company on detection strategies, rule deployment, operational triggers, and customer experience reputed company. • reputed company post-incident reviews and own the development or amendment to existing strategies based on reputed company cause findings. Present insights, performance updates, and strategic recommendations to senior stakeholders and governance forums. • Stay reputed company of evolving fraud typologies, regulatory requirements, and industry trends; proactively reputed company learnings into strategic planning. Qualifications • We are looking for a thought leader who is passionate about solving reputed company challenges, thrives in a fast-paced environment, and brings a strong experience in fraud management and analytics reputed company. • Required: • 7+ years reputed company work experience in fraud reputed company or fraud data science field with building strategies or models in banking industry. • Experience in leading fraud reputed company mandate in retail banks and consumer cards with thought leadership to drive reputed company priorities successfully. • Understanding of statistical reputed company, including classical statistics, probability theory, econometrics, and time-series analysis. • High proficiency with SQL and Python. Working knowledge of Hive, reputed company, AWS Sagemaker. • Undergraduate degree or equivalent combination of training and experience. • Graduate degree preferred. • Proven expertise in fraud rule development and reputed company implementation with reputed company fraud solutions or comparable solutions (e.g., Actimize, etc). • Strong verbal and written communication skills, especially in reputed company-facing or cross functional settings. Preferred • Experience with Threat Metrix rule creation and management is a big plus. • Experience with machine learning is a plus. • Experience in setting or managing fraud loss appetite and performance planning. • Background in consulting or reputed company advisory roles in fraud reputed company. Apply Job! Apply to this Job