Senior Data Scientist Vice President

Business Analytics Lead Analyst – Fusion Analytics Team

The Business Analytics Lead Analyst is a member of the Fusion Analytics Team under FFC. This role will be tasked with creating analytical solutions across all LOBs utilizing statistical and advanced analytical techniques and drive strategic insights and decision making. The ideal candidate will have strong expertise in machine learning, predictive modeling and financial data analysis. Experience in digital fraud detection and cybersecurity will be a strong advantage.

The role involves collaborating with cross‑functional teams to develop and implement data‑driven solutions.

• Creating analytical solutions to mitigate losses across all LOBs using statistical/advanced data science techniques.
• Analyzing compromised card data from dark web to identify emerging fraud trends, suspicious patterns, anomalies, high‑risk merchants, locations and transaction behaviors.
• Leveraging AI/ML models (e.g., anomaly detection, graph neural networks, NLP) to anticipate fraudulent behavior.
• Implementing AI‑powered automation to improve fraud detection efficiency.
• Developing and enhancing data models and algorithms to identify high‑risk accounts for proactive monitoring and closure; assessing impact of compromised cards on fraud losses and quantifying risk exposure.
• Collaborating with threat intelligence teams to incorporate external fraud signals into risk models, identifying fraud rings, mule accounts, synthetic identities by linking compromised data to existing customer portfolios.
• Generating executive‑level insights and reports for leadership using advanced visualization techniques, providing regular updates on fraud trends and emerging threats, and delivering actionable insights to senior global stakeholders.
• Leading POC’s with new vendors, evaluating fraud detection tools, data enrichment platforms and dark web monitoring solutions.
• Performing ad‑hoc analysis on large, unstructured datasets (e.g., transaction logs, dark web feeds) to identify fraud indicators using Python, SQL and SAS.
• Managing significant fraud events by coordinating information sharing across financial crime and fraud prevention organizations, partnering with various cross‑functional teams to design intelligence‑derived solutions.
• Collaborating with fraud analytics modelling functions to understand new fraud detection capabilities and develop new analytical solutions leveraging unstructured data sets and variables.

• Bachelor’s degree in engineering, statistics, economics, finance, mathematics or a related quantitative field from a premier institute. Master’s degree is beneficial but not required.
• Minimum 5+ years of relevant experience in data analysis, data mining or statistical analysis.
• Working knowledge of Python, SQL, Teradata, RDBMS, Hadoop/Hive tools.
• Experience with statistical software packages:
  Python (preferred), SQL, SAS (required).
• Experience with AI/ML frameworks (Tensor Flow, PyTorch, Scikit‑learn).
• Knowledge of large language models for text analysis and fraud intelligence.
• Experience in predictive modelling, statistical analysis and machine learning techniques.
• Ability to analyze large‑scale, unstructured data and generate actionable insights.
• Familiarity with digital fraud detection tools and experience working with cybersecurity datasets and threat intelligence platforms.
• Prior experience developing dynamic dashboards using visualization tools such as Tableau.
• Data science experience in any risk domain is preferable.
• Experience identifying fraud patterns in large consumer banking portfolios.
• Demonstrable analytical, problem‑solving and leadership skills with ability to deliver projects in a fast‑paced environment.
• Excellent quantitative and analytical skills, data‑driven mindset, and ability to derive patterns, trends and insights and perform risk/reward trade‑off.
• Ability to effectively collaborate with cross‑functional partners and management.
• Solution‑oriented “can do” attitude with ability to drive innovation via thought leadership while maintaining an end‑to‑end view.
• Extremely detail‑oriented, intellectually curious, and able to multi‑task in a fast‑paced…