Sr Risk Analyst

This role requires deep statistical expertise, advanced data science capabilities, and strong business acumen to translate data-driven insights into effective models that balance growth, profitability, and risk mitigation. Additionally, it requires strong consultative and relationship-building skills, the ability to influence senior stakeholders, and the ability to bridge the gap between technical data science and business strategy.

Key Responsibilities

End-to-End Model Development & Innovation

• Lead the design, development, and validation of predictive models across the banking lifecycle, which may include:
• Risk/Decision Models: Credit scoring, underwriting frameworks, behaviour modelling, collection/loss mitigation, and fraud detection.
• Marketing/Growth Models: Propensity/response modeling, customer segmentation, attrition/churn forecasting, Next-Best-Action, and Customer Lifetime Value (LTV).
• Perform detailed portfolio analysis using internal customer transactional data, bureau attributes, digital footprints, and alternative data sources to optimize business decisions.
• Drive innovation by leveraging advanced statistical techniques, machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks), and automation to strengthen the client's analytical capabilities.

Strategy, Governance & Stakeholder Collaboration

• Deliver interactive MIS reports, performance dashboards, and model reviews to monitor model health, track population stability (PSI), assess characteristic drift (CSI), and evaluate ROI/strategy effectiveness.
• Collaborate with client stakeholders and cross-functional teams (Product, Risk, Marketing, Finance, Compliance, and Technology) to align modeling frameworks with core business objectives.
• Ensure strict adherence to regulatory and governance requirements, including Model Risk Management (MRM) guidelines (e.g., SR 11-7), consumer privacy laws, and fair lending practices.
• Provide clear insights and strategic recommendations to senior leadership on model performance, emerging trends, and forward-looking data strategies.

Account Management & Business Development

• Build and nurture long-term client relationships by understanding their core analytical pain points and positioning EXL’s modeling solutions effectively.
• Identify opportunities to expand engagements through the cross-sell/up-sell of advanced analytics, data engineering, model validation, and strategy consulting services.
• Partner with business development teams to support proposal creation (RFPs), client pitches, and the definition of value-driven project outcomes.

Qualifications

• Education: Bachelor’s degree in Statistics, Mathematics, Data Science, Economics, Operations Research, Engineering, or a highly quantitative field.
• Experience: 7 years of experience in predictive modeling, model development, or data science within management consulting, banking, or financial services.
• Domain Expertise: Strong understanding of retail banking and consumer lending products (credit cards, personal loans, mortgages, auto loans) and their lifecycle dynamics.
• Technical Proficiency:
• Advanced proficiency in Python, R, SQL, or SAS for data manipulation and statistical modeling.
• Strong hands-on experience with regression, classification, clustering, time-series forecasting, and machine learning frameworks.
• Familiarity with data visualization tools like Tableau or Power BI is a plus.
• Communication & Collaboration: Excellent communication and presentation skills, with a proven ability to translate complex data science concepts into actionable business strategies for executive stakeholders.
• Regulatory Knowledge: Solid understanding of model risk management frameworks (e.g., SR 11-7), compliance standards, and data governance in a regulated banking environment.
• Client & Growth Mindset: Proven experience managing client relationships and contributing to business development efforts, proposal writing, or solution architecture.