Lead Decision Science Analytics Consultant

• Lead the development and validation of models supporting financial valuation for Credit Card, Personal Loan, and Retail Services products
• Perform in-depth analytical assessments to evaluate and validate key model inputs, assumptions, and parameters
• Support strategic initiatives aimed at redesigning valuation frameworks and models to enhance investment decision-making capabilities and drive business transformation
• Ensure adherence to a robust control environment by maintaining compliance with model risk management and documentation standards
• Serve as a key technical expert within the valuation modeling team, driving the adoption of advanced analytical capabilities using Python and emerging AI/ML tools
• Lead complex initiatives related to business analysis and modeling, including those that are cross functional, with broad impact, and act as key participant in data aggregation, monitoring, and reporting of stress testing and portfolio analysis for Risk Analytics
• Review and analyze complex programing models to extract data and manipulate databases to provide statistical and financial modeling specific to businesses supported
• Collaborate and consult with peers, less experienced to more experienced managers, to resolve production, project, and regulatory issues, and achieve risk analysts, and common modeling goals
• Lead projects, teams, and mentor

5+ years of Risk Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• Advanced programming experience in Python and/or SAS, with a focus on building scalable analytics and modeling solutions
• Experience developing or validating forecasting, valuation, or credit risk models within financial services or lending environments
• Familiarity with unsecured lending products (Credit Cards, Personal Loans, Retail/Consumer Finance) and their underlying risk dynamics
• Strong foundation in statistics, economics, and financial valuation methodologies, with experience applying both qualitative and quantitative approaches
• Experience working with large, complex datasets and modern data environments (e.g., SQL, cloud platforms, or distributed computing frameworks)
• Exposure to model governance, model risk management (MRM), or regulatory frameworks within a financial institution
• Experience leveraging or integrating machine learning techniques into traditional modeling approaches
• Proven ability to lead cross-functional projects and influence stakeholders across technical and non-technical teams
• Strong communication skills with the ability to translate complex analytics into clear business insights and recommendations