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.