Principal Data Analyst - Rural Analytics

We are looking for an experienced Principal Data Analyst to lead complex analytics initiatives within the Rural Analytics team. The role requires a strong combination of advanced analytics, statistical and machine learning modelling, banking/business understanding, stakeholder management, and team leadership. The individual will independently own critical analytical workstreams, translate complex business problems into data-driven solutions, and work closely with senior stakeholders to increase the adoption of analytics across business decision-making.

The core responsibilities for the job include the following:

Advanced Analytics and Business Insights:

• Lead analytics initiatives across banking products covering areas such as risk, customer propensity, pricing, portfolio performance, and customer targeting.
• Translate complex business problems into structured analytical approaches and actionable recommendations.
• Analyze large and complex datasets to identify customer behavior, portfolio trends, business opportunities, and risk indicators.
• Generate actionable insights that improve business performance, customer outcomes, portfolio quality, and decision-making.

Predictive Modelling and Scorecards:

• Build and enhance application, behavior, and propensity scorecards using traditional statistical as well as machine learning techniques.
• Develop predictive models to support customer acquisition, targeting, risk management, pricing, and portfolio strategies.
• Own the end-to-end analytics lifecycle, including data collection, data preparation, exploratory analysis, model development, validation, interpretation, and insight generation.
• Work hands-on with analytical tools such as Python, R, SAS, and other relevant modelling platforms.

Customer and Portfolio Analytics:

• Design analytical solutions to identify the right customer, the right offer, and the right channel to maximize conversion and customer engagement.
• Develop data-driven strategies to improve portfolio quality and manage delinquency.
• Support the development and continuous improvement of business and risk decision strategies.
• Track the performance of implemented analytical strategies and recommend enhancements based on outcomes.

Stakeholder Management and Decision Support:

• Partner closely with Product, Sales, Credit, Operations, Technology, and other cross-functional stakeholders.
• Clearly communicate analytical findings, model outputs, recommendations, and business implications to both technical and non-technical stakeholders.
• Drive greater adoption of analytics and analytical models in business decision-making.
• Work with internal teams and external partners to build, implement, track, and improve analytical solutions.

Leadership and Project Ownership:

• Independently own complex analytical workstreams with minimal supervision.
• Manage projects from problem definition through analysis, implementation, and performance tracking.
• Mentor and guide team members to ensure high-quality and timely delivery.
• Establish effective analytical processes and ensure adherence to internal controls and documentation requirements.
• Conduct research and evaluate new analytical approaches relevant to business problems.

Requirements:

• 10-12 years of relevant experience in data analytics, advanced analytics, risk analytics, customer analytics, or related areas.
• Strong analytical thinking and structured problem-solving capabilities.
• Strong experience in predictive modelling and statistical analysis, including traditional and machine learning techniques.
• Hands-on experience with Python; exposure to R and/or SAS is valuable.
• Experience building application, behavior, propensity, or similar analytical scorecards.
• Strong understanding of translating analytical outputs into business strategies and decisions.
• Excellent stakeholder management and communication skills.
• Ability to independently lead complex analytical projects.
• Strong people management, mentoring, and team leadership capabilities.
• Graduate or postgraduate qualification.
• An MBA / master's degree in economics, statistics, analytics, or a related quantitative discipline is preferred.
• A B. Tech / Bachelor's degree in computer science or another relevant discipline is also suitable.

Preferred Qualifications

• Candidates with experience in banking, financial services, lending, risk analytics, customer analytics, portfolio analytics, or other data-intensive financial services environments will be particularly relevant.

Skills

advanced analytics, customer analytics, data analytics, machine learning, predictive modelling, python, risk analytics, statistical analysis