VP- Rsik Analytics

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Experience: 12+ yrs

Location: Bengaluru, Karnataka, India

Job Type: Full-time

We are looking for an accomplished and data-driven VP – Risk Analytics to lead the development and execution of advanced risk analytics strategies across credit portfolios. This senior leadership role will focus on strengthening credit risk management, improving portfolio quality, proactively managing delinquency, and leveraging data science, AI, and advanced analytics to support profitable and sustainable business growth.

The ideal candidate will combine deep expertise in credit risk and portfolio analytics with strong leadership and a modern understanding of data science and AI. You will partner closely with business, risk, product, technology, finance, and senior leadership teams to translate complex data into actionable insights and strategic decisions.

KEY RESPONSIBILITIES

• Lead the overall credit risk analytics and portfolio analytics strategy across the organisation.
• Develop frameworks to monitor portfolio quality, credit performance, delinquency, roll rates, vintage performance, and risk trends.
• Identify key drivers of delinquency, defaults, losses, and portfolio deterioration and develop proactive mitigation strategies.
• Lead development and enhancement of credit risk models, scorecards, segmentation, propensity models, and early-warning systems.
• Apply data science, machine learning, AI, and predictive analytics to strengthen credit decisioning and portfolio management.
• Develop portfolio monitoring dashboards, risk indicators, forecasting models, and management reporting for senior leadership.
• Analyse customer, product, geographic, channel, and behavioural segments to identify emerging risk patterns and growth opportunities.
• Partner with Credit, Risk, Collections, Product, Business, Finance, and Technology teams to translate analytics into business actions.
• Establish robust analytical approaches for underwriting, limit management, portfolio optimisation, collections, and risk-based customer strategies.
• Drive experimentation and continuous improvement of analytical models, methodologies, and decision frameworks.
• Ensure appropriate model validation, monitoring, governance, documentation, and performance tracking.
• Build and lead high-performing teams across risk analytics, data science, and advanced analytics.
• Present portfolio insights, risk trends, forecasts, and strategic recommendations to executive leadership.
• Identify opportunities to automate analytical processes and improve the speed and scalability of risk decision-making.
• Stay current with emerging developments in AI, machine learning, alternative data, and advanced credit-risk analytics.

WHAT MAKES YOU A GREAT FIT

• 12+ years of experience in credit risk, risk analytics, portfolio analytics, data science, or related financial-services analytics roles.
• Strong and demonstrated expertise in Credit Risk, Delinquency Analytics, and Portfolio Analytics.
• Deep understanding of credit lifecycle, underwriting, portfolio monitoring, collections, defaults, loss behaviour, and risk segmentation.
• Proven experience developing and implementing credit risk models, scorecards, predictive models, and early-warning systems.
• Strong hands-on understanding of data science, machine learning, AI, statistical modelling, and predictive analytics.
• Experience applying advanced analytics to solve complex credit and portfolio-management problems.
• Strong ability to interpret large and complex datasets and translate analytical insights into clear business recommendations.
• Experience leading and mentoring high-performing risk analytics and data science teams.
• Strong stakeholder-management skills with the ability to influence senior business and risk leaders.
• Excellent understanding of portfolio performance metrics, delinquency trends, risk-adjusted returns, and credit risk indicators.
• Strong analytical, strategic, problem-solving, and decision-making capabilities.
• Experience working with cross-functional teams across Risk, Credit, Collections, Product, Technology, Finance, and Business.
• Strong communication and presentation skills, with the ability to explain complex analytical concepts to non-technical stakeholders.
• A strategic and forward-looking mindset with a strong interest in applying AI and emerging technologies to modern risk management.
• Advanced proficiency in analytics tools, SQL, Python/R, statistical modelling, and data platforms is highly desirable.
• MBA, Master's, or Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, Finance, or a related discipline is preferred.