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 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 modelling, 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. #J-18808-Ljbffr