Risk Modeler

Description

We are looking for a results-driven and intellectually curious modeling professional to join a dynamic Advanced Analytics team within the Risk function of a growing financial services organization. This position plays a key role in designing and deploying predictive models and data-driven strategies that influence credit policy, optimize business decisions, and drive portfolio performance. Although the role is embedded in the Risk group, collaboration extends across marketing, finance, operations, and other key business units.

This is a great opportunity for someone with a solid foundation in quantitative modeling, data analysis, and business strategy who thrives in fast-paced, analytically sophisticated environments.

Key Responsibilities

• Design and implement predictive models and strategic frameworks to manage risk, improve acquisition targeting, forecast losses, and support credit policy decisions.
• Apply advanced statistical and machine learning methods, including but not limited to logistic/linear regression, time-series (ARIMA/GARCH), tree-based models, gradient boosting, and unsupervised learning.
• Build end-to-end modeling solutions-from development to implementation, monitoring, and performance optimization.
• Lead the testing and evaluation of risk strategies, model-driven decision rules, and new data attributes to assess their business impact.
• Analyze large, complex datasets to extract insights and generate actionable business recommendations.
• Collaborate closely with business stakeholders, technology teams, and data engineers to align models with enterprise goals and technical infrastructure.
• Produce detailed performance reports to assess model stability and efficacy over time.
• Offer expert-level consultation to leadership on quantitative strategy, model governance, and analytical best practices.
• Track economic and industry trends to evaluate their impact on risk exposure and business profitability.
• Communicate technical findings clearly to both technical and non-technical audiences, using written reports and visualizations.

Requirements

Qualifications

• Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Economics, Finance, etc.) with 2+ years of relevant experience, or an advanced degree (Master's or PhD) in the same fields.
• Strong knowledge of predictive modeling techniques and experience applying them to real-world business problems.
• Proficiency in Python, R, SAS, and SQL for data analysis and statistical modeling.
• Knowledge of compliance frameworks like Archer and CobiT
• Exceptional analytical, conceptual, and communication skills.
• Strong attention to detail, with the ability to manage multiple priorities and meet tight deadlines.
• Comfortable working in collaborative environments as well as independently driving initiatives forward.

Preferred Qualifications

• Advanced degree (MS/PhD) with 4+ years in analytics or financial services modeling.
• Experience building models for consumer credit risk, portfolio management, revenue forecasting, or marketing optimization.
• Familiarity with financial statement analysis, macroeconomic modeling, and regulatory environments.
• Hands-on experience developing models such as:
• Acquisition or risk scoring models
• Loss forecasting (vintage-based or macro-driven)
• Offer optimization for credit or lending products
• Provision modeling and VaR frameworks

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