Lead day-to-day delivery for Risk Analytics and Data Quality workstreams, balancing people leadership with hands-on analytical execution.
Supervise, coach, and develop a team of domestic and offshore data professionals; coordinate workload, priorities, and delivery commitments.
Partner with stakeholder teams across Risk Analytics and Enterprise Data Management to scope work, set expectations, and deliver results.
Own project planning, execution, and reporting for assigned initiatives; manage resources and risks to meet timelines and quality expectations.
Establish and maintain standards for documentation, reproducibility, scalability, and model/data governance.
Plan, develop, and deliver analytical models including classification and predictive models, scoring and rules-based models, and other advanced analytics techniques (machine learning and artificial intelligence).
Perform problem framing and analysis; lead data collection, integration, exploration, and preparation to support modeling objectives.
Guide model implementation in partnership with technology and business teams, ensuring solutions are production-ready and measurable.
Support analytics needs across Fraud Prevention, Anti-Money Laundering (AML), Compliance, Credit Risk, Market Risk, Operational Risk, and Finance.
Apply appropriate methodology across the model lifecycle, including tracking, documentation, reproducibility, scalability, monitoring, and actionable insights.
Develop and oversee analytical controls and reporting to identify and track data-flow issues across systems and data sources.
Define and monitor critical data elements; detect unexpected values and potential quality defects.
Drive issue triage and resolution by partnering with stakeholders; track remediation through to closure.
Requirements
10+ years of experience in banking and analytics, including senior-level stakeholder engagement and delivery ownership.
Graduate degree in Statistics, Data Science, Applied Economics, Machine Learning, or a related field (or equivalent experience).
Strong foundation in statistics, data science, and modern analytical techniques, including machine learning and AI concepts.
Proficiency with analytical programming and data tools such as Python, SAS, R, and SQL.
Experience leading teams and delivering work through clear planning, prioritization, and execution.
Excellent written and verbal communication skills, with the ability to explain complex analytical topics to technical and non-technical audiences.
Proficiency with Windows productivity tools (e.g., Microsoft Office).
Tech Stack
Python
SQL
Benefits
Health insurance
Paid time off
Lead Commercial Product Manager – Risk Analytics, Data Quality at FIS | JobVerse