Lead Commercial Product Manager – Risk Analytics, Data Quality

Role Overview

• 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